creates a LOS AND if the time to LOS (tLOS) is within two minutes. ... pCPA and the tLOS criteria were not met, no separation assurance maneuver is required.
A Fuzzy Logic Approach for Low Altitude UAS Traffic Management (UTM) Brandon Cook1, Kelly Cohen2, Elad Kivelevitch3 University of Cincinnati, Cincinnati, OH 45221
In the coming years, operations in low altitude airspace will vastly increase as the capabilities and applications of Unmanned Aerial Systems (UAS) continue to multiply. Therefore, solutions to managing vehicles in highly congested airspace must be explored. In this study, an intelligent systems approach was used to help mitigate the risk of collision between aircraft in uncontrolled airspace using a UAS Traffic Management (UTM) System. To test the effectiveness of this system, a three-dimensional environment was created using MATLAB to simulate a fully autonomous heterogeneous fleet of UAS attempting to accomplish a variety of realistic missions, including precision agriculture, package delivery services, natural resource monitoring, and disaster management. Main research challenges include situational awareness, decision making, and multi-agent control in an uncertain, time-critical, spatio-temporal environment. The UTM system was evaluated for its effectiveness at using intelligent separation assurance and collision avoidance techniques to mitigate the risk of Near Mid-Air Collisions between aircraft. This fuzzy solution utilizes only current state information and can resolve potential conflicts without knowledge of intruder intent. It was found that the collision avoidance techniques were 99.88% successful over a span of nearly 16,255 flight hours. Lastly, it was found that the techniques employed for separation assurance drastically mitigated the risk for a loss of well-clear. Comparing the unmitigated and mitigated cases, the number of losses of separation between aircraft reduced from one loss of separation per two flight hours, to one loss of separation per ten flight hours. This mitigated separation assurance platform was successful at preventing a loss of separation 88.47% of the time, over a span of 7,545 flight hours.
I. Introduction
I
N recent times there have been substantial advances in the capability of mobile robots in several Aerospace applications. However, despite the vast benefits these technologies will have on society, these advancements are currently being under-utilized due to several safety issues that have yet to be addressed. That being said, a clear and present need has arisen for a heterogeneous team of UAVs to interact autonomously and perform time-critical tasks in complex environments (e.g. large scale disaster management, search and rescue over land and sea, monitoring natural resources, delivery services in urban environments, etc.). As the applications and capabilities of UAVs proliferate over the coming decades for both civilian and military uses, it is imperative to address the safe integration of these vehicles into the National Airspace System (NAS). In the scope of this research, we aim to research and develop a method for controlling a group of UAS using a unique, bio-inspired, hybrid approach based on Fuzzy Logic. By developing this Intelligent System (IS) capable of real-time situational awareness, we can mitigate the risks of Near Mid-Air Collisions (NMACs) between aircraft. In order to investigate the architecture and algorithmic aspects of the bio-inspired cooperative system, it is necessary to create a realistic simulation environment that enables the user to define and test different concepts of operations for UTM. In this simulation environment, the developer can employ various algorithms/techniques to solve the many problems associated with coordinating vehicles in highly congested airspace. This enables various UAS platforms, hardware packages, software packages, and management methodologies to be tested and evaluated. Of the many applications that small UAS will be used for in the future, a handful of the more popular tasks were chosen for this simulation. These applications include disaster management operations (search and rescue missions), 1
Graduate Student, Aerospace Engineering, AIAA Student Member Professor, Department of Aerospace Engineering, AIAA Senior Member 3 Assistant Professor - Educator, Department of Aerospace Engineering, AIAA Senior Member 2
package delivery services, monitoring of forestry for wild-fire prevention, precision agriculture, and roadway surveillance. Each of these operations will operate simultaneously in the selected airspace with heterogeneous vehicles. This heterogeneous system is comprised of two aircraft models, that is, one fixed wing model and one multi-rotor model. Moreover, for this research the interoperability between UAS and manned aircraft are not tested. Therefore, we are assuming that all UAS operations are at low altitudes (below 500 feet), in class G airspace, and shall not come within 5 nautical miles of an airport. The primary goal of this research is to develop a high level concept of operations for a UTM system. This system must address the issues of collision avoidance and separation assurance techniques. Each of these systems will be developed using a Fuzzy architecture to enable real-time decision making and dynamic control.
II. Literature Survey A. Separation Assurance In current day operations for separation assurance in the NAS, air traffic control (ATC) takes the primary responsibility for safe operation of all manned aircraft. ATC has active control over each vehicle inside a designated airspace and issues flight path deviations to keep them separated from other traffic1. This in turn limits the number of aircraft that can safely operate in a given airspace at a given time. Seeing as these restrictions put a bottleneck on current airspace capacity, new ground-based automated services for the Next-Generation Air Traffic Management system have been developed to help resolve these limitations. With this new technology, the safety of a scheduled flight plan is monitored with a 20 minute look ahead time. If at any point in time a potential loss of separation (LOS) is recognized, a strategic trajectory modification is created for that aircraft using an algorithm known as “auto-resolver”2. In this algorithm, a pre-allocated set of candidate solutions are evaluated. Then the candidate solutions that can successfully resolve the potential LOS are compared upon a designated metric (e.g. minimum lateral deviation from flight path, minimum time off current route, etc.). Once the resolution is selected, the ground-based automated system uploads the new flight plan into the vehicles flight software. After extensive tests using human-in-the-loop simulations with this software, it is evident that this approach is effective at mitigating the risk of LOS. Although, these techniques do in fact solve manned aircraft conflicts, the resolution is based on trajectory information and time to LOS. In the scope of this project, we want to explore separation assurance techniques for aircraft that do not have information about an intruder‟s specific trajectory, intent, and do not have scheduled flight plans. Therefore, these types of operations would allow UAS controllers to fly vehicles in class G airspace without filing an explicit flight plan. These assumptions would allow small UAS to fly in the NAS despite their low Maximum Take-Off Weight (MTOW) and hardware restrictions. B. Collision Avoidance One popular approach for resolving Near-Mid Air Collisions is using a probabilistic perspective. By formulating the problem of collision avoidance through the use of a Markov Decision Process (MDP) or a Partially Observable Markov Decision Process (POMDP), avoidance strategies can be developed based on the onboard sensor performance, intruder behavior, and aircraft dynamics. This approach uses a discrete time stochastic process that utilizes only current state information to predict the future state of a model and determine a suitable action. In a study conducted by Temizer et al., these techniques proved to be able to produce complex policies in real-time and was effective at achieving state estimation and policy execution3. Two additional research efforts worth mentioning similarly use an intelligent systems approach for collision avoidance between UAVs. In the first study conducted by Durand et al., a neural network (NN) was developed to solve conflict pairs and then the system was trained using a Genetic Algorithm 4. In the second, Hromatka developed a Fuzzy Logic collision avoidance system for UAVs5. Here, many of the same assumptions were made as those in our study, however, the vehicle platform limits aircraft to a maximum speed of 25 mph (or ~11.2 m/s); whereas, in our study we analyzed vehicles traveling upwards of 100 m/s. Although vehicles were traveling at a slower speed, which ultimately reduces the maximum closure speeds between vehicles, the operational flight area is smaller. This inevitably should lead to an increased number of conflicts between vehicles; however, the total flight hours for simulation were not presented, so a direct comparison could not be made. Overall, the results of each intelligent systems approach produced favorable results. While many algorithms and approaches have been used for developing a collision avoidance system for UAVs, one in particular has shown promising results and has been tested for several human-in-the-loop simulations. Although this technology is being developed for the Next-Generation Air Traffic Control system, the approaches used would be directly applicable to automating close-range separation assurance for UAVs. This algorithm, named
“autoresolver”, was developed to resolve short-range conflicts to prevent a loss of separation between vehicles. Although the main focus for the research described in this study addresses collision avoidance after a loss of separation has occurred, autoresolver focuses on computing potential resolutions that will solve a conflict between aircraft prior to a loss of separation. This software has been developed to help automate many of the tasks that an air traffic controller currently performs 2. Seeing as current systems utilize a human-in-the-loop or human-on-the-loop approach, autoresolver is state-ofthe-art for such systems when combined with two additional components known as tactical separation assured flight environment (TSAFE)6 and traffic alert and collision avoidance system (TCAS)7. If given a sufficient amount of time to solve a conflict, autoresolver will upload a resolution advisory (RA) to the vehicle and display the computed resolution to the operator. However, if the user does not maneuver the aircraft in a timely manner (i.e. if the time to loss of separation falls below a defined threshold), the secondary system (TSAFE) will automatically uplink its maneuver to the aircraft6. Lastly, if TSAFE fails to resolve the conflict, TCAS serves as the final line of defense, which is typically activated when the predicted time to collision is less than two minutes. In a study evaluating the performance of autoresolver and TSAFE, both cooperative and single aircraft maneuvers were generated using horizontal resolution trajectories to maximize the closest point of approach (CPA)6. Similar to the results presented in this paper for the no priority case, TSAFE can resolve conflicts more effectively when both aircraft are performing a maneuver. Each of these algorithms utilize aircraft turn rate restrictions and geometric descriptions of conflict scenarios to determine a particular resolution, again, similar to the approaches utilized in this study.
III. Simulation Environment C. Airspace Description The simulation environment created for this research models a section of the US airspace that will see high volumes of activity in low altitude airspace in the coming years. This high traffic volume will be a result of the large variability in land types. In this airspace we see both urban and rural communities, national parks, bodies of water, high activity of natural disasters (tornadoes), and an abundance of agriculture. These features will encourage operations of UAS including, but not limited to: disaster management, package delivery services, precision agriculture, roadway surveillance, monitoring and prevention of wild-fires, and a variety of science missions.
Figure 1. Representation of Selected Airspace
A depiction of the selected airspace, found using Google Earth, can be seen in Figure 1. This airspace encompasses ~2,500 square miles with a maximum altitude of 500 ft. AGL and spans over central Ohio. The outline of the airspace is depicted in red and acts as a geo-fence for this UTM zone. Inside this airspace one can see various portions sectioned off. Here, the three orange sections represent agricultural zones and the green section represents a
national forest (Wayne forest). In addition to these zones, there are other icons shown on the figure that designate depots for various missions. In the West corner of the airspace there is an icon labeled “DMC”. This represents one of the “Disaster Management Centers”, where UAVs are stationed to help aid with search and rescue missions. This hub has been tactically placed at this location due to its close proximity to Xenia, Ohio, which is notorious for heavy tornado activity in that region. In the Northern section of the airspace one can see the “Delivery Hub”. From here, UAVs will be sent out to deliver packages to homes and businesses. Lastly, there is an icon at the center of the map titled “UTM Center” (UAS Traffic Management Center). The UTM Center is the site that coordinates all of the inflight adjustments of the unmanned aircraft, and houses the off-board “brains” of the intelligent system. D. Mission Types and Objectives In this designated airspace many UAVs will be active, each with individual responsibilities and missions. Throughout the simulation, the UAVs will travel to various waypoints to accomplish their assigned tasks. As the simulation progresses, the system will have to adapt to various conditions such as adding agents, avoiding collisions between aircraft, and adhering to airspace regulations such as right-of-way rules and altitude restrictions. The multiagent system will collaborate as a group, rapidly explore the solution space, and adapt to changes in the simulation. Once all waypoints for a given mission have been completed, the aircraft will return to their respective starting locations. In each of the applications previously described, there are two different types of missions that can be deployed: surveillance and on-station. When a surveillance mission is assigned to a designated zone, the UAS must explore the entirety of that area, whereas an on-station task needs a UAS to stay on site for a given amount of time. E. Scalability and Operations The MATLAB code is versatile enough to input any number of UAVs and waypoints; however, in order to compare results of the various algorithms and environments, these numbers were held consistent throughout the scope of this paper. In each test case a maximum of 184 UAS could be in the air at any given time. These items could be altered by the user, but if increased the simulation time will increase accordingly. This is due to having a “brain-off-board” global simulation environment. In a realistic environment, some of the computationally heavy components can be handled in parallel on-board of each individual UAV. Each application type has a designated number of operational depots where UAVs can take off and land. For each agricultural zone, there are four depots. For the forest zone there are eight depots. For the road monitoring, there are 14 depots. For the delivery service there are ten depots. And lastly, for the disaster management scenario there are eight depots. Prior to beginning the simulation, the user has the ability to set how many UAS would be used for each mission. While every survey mission utilized the full number of fixed wing UAS to complete the task, each multi-rotor on station mission only used the necessary amount of UAS to complete its mission. Therefore, if an on station mission only had 6 targets of interest, but had 12 UAS available, only 6 UAS would be used to monitor those waypoints of interest, however, if a 12 or more targets needed to be visited to complete the mission, all 12 UAS were deployed. Table 1 describes the number of UAS available for each mission type. As previously mentioned, although available, the multi-rotor cases will not always utilize this maximum number of vehicles. Since there are three agriculture zones, this results in a maximum of 184 aircraft that can be operating in the airspace at any given time. Table 1. Number of UAS per Mission
Fixed Wing Multi-Rotor
Precision Agriculture
Forest Monitoring
Roadway Surveillance
Disaster Management
Package Delivery
8 8
16 16
28 28
12 12
12 12
Once the simulation has been initiated, a random event generator is used to decide when each mission is active. Once a mission has been completed, it may be reassigned to a new mission at any point in time. Furthermore, if at any point in time a collision between two or more aircraft occurs, the simulation is stopped and reset. F. Aircraft Models 1. Constraints The UAS fleet is comprised of two vehicle platforms that make up the heterogeneous system: fixed wing and multi-rotor. For each of these vehicle platforms kinematic models were used to characterize and constrain the movement of each aircraft. Some of these constraints include maximum load factor, maximum turn rate, maximum
climb/decent rate, maximum dash speed, minimum cruise speed, and maximum altitude. These assumptions allow each vehicle to be treated as a point mass. High fidelity models were not created as this was not the focus of this research. Although the simulation does model three-dimensional flight, for this phase of research each aircraft is assumed to climb to an altitude of 500 ft. at the start of its mission. Here, each aircraft may only perform lateral deviations and speed adjustments from its ideal flight path to ensure safe separation of the vehicles and for collision avoidance maneuvers. This allows us to assume level, two-dimensional flight when executing these types of maneuvers. Using this assumption allows the maximum turn rate for each fixed wing vehicle to be found using the following equation: ̇
√
(1) For the fixed wing platform, the vehicles are constrained to maintain a flight speed between 30 and 60 knots and must not exceed a load factor of 3.5. Furthermore, the fixed wing aircraft has a maximum climb rate of 2 ft./sec. In the multi-rotor systems we have assumed that the vehicles can travel at a maximum airspeed of 38 knots and a maximum climb rate of 2 ft./sec. These vehicles do not, however, have a minimum airspeed requirement and can thus hover in one location. Due to the nature of the system, Equation 1 does not accurately model the maximum turn rate for this vehicle type. Thus, it is assumed that the aircraft can yaw at a maximum rate of 45 deg/s. Although each platform is governed by different kinematic constraints, the sensors and hardware on-board each platform are the same. The two primary hardware packages worth mentioning include a sensor for detection of objects within a close proximity to the vehicle and a transmitter/receiver to allow communication between the vehicle and the UTM Center. 2. Sensor and Hardware Assumptions Although no particular senor model or hardware component was selected and modeled in this simulation, general assumptions and approximations have been made for each UAV component of interest. Given the aim of this research is to design a UTM system geared towards future builds, the sensors and hardware used by the UAVs in this study were assumed to be beyond current state-of-the-art technology capabilities. That being said, each aircraft has assumed to have the capability to detect an intruding aircraft at a distance of 0.1 nmi if within a 180° field-of-view in front of the aircraft. With this object detection, the aircraft has the ability to measure the intruding aircrafts relative velocity, heading, and distance. This on-board sensor, or combination of sensors, is used as the primary component for the aircraft‟s sense-and-avoid (SAA) technology. Although sensor uncertainty models have not been used throughout this work, a “collision buffer” has been defined to simulate that the aircraft may not have perfect knowledge of the intruder‟s location. Therefore, if any two aircraft come within this collision buffer a collision is assumed to have occurred. This collision buffer has been defined as aircraft coming with 60 m of each other. In current day ATM applications an NMAC is defined to occur when two aircraft are separated by 500 ft. or less. When comparing this near collision zone to the 60 meter, or ~200 ft., standard used in this research, this standard of separation may be a bit conservative. When analyzing the separation of small UAS, 200 ft. may be sufficient due to the small closure rates between these vehicles. However, if you consider the interoperability between manned aircraft and small UAS, this standard would most likely be much larger. In addition to this on-board SAA capability, each aircraft is also assumed to have reliable communication capabilities. In practice, this may be a combination of ADS-B, 4-G cellular network channels, or other transponder sources. Given these communication capabilities, each UAS has the ability to transmit telemetry data to a ground based UTM system. Furthermore, it is assumed that ground sensors used to detect aircraft have also been distributed throughout the airspace. These sources, coupled with the self-reporting of telemetry data, are assumed to provide continuous and reliable information about the aircraft position, heading, and speed. This communication capability is used to allow for separation assurance practices to be employed. These separation assurance capabilities are assumed to be active whenever aircraft come within 0.4 nmi, and have predicted to have a LOS in the future. Therefore, it is assumed that whenever aircraft are within this 0.4 nmi slant range, the UTM system can detect, calculate, and resolve a potential LOS. Then, once the resolution has been calculated, the Resolution Advisory (RA) will be uploaded directly to the UAS, causing a deviation from flight path to mitigate this risk of LOS.
IV. UTM Controller Design In this section, the individual components of the UTM system are described. First the collision avoidance techniques are described. Next, a secondary layer to help mitigate the risk of Near-Mid-Air Collisions was developed using separation assurance techniques. A. Collision Avoidance Software
1. Conflict Classification The first step to developing the collision avoidance package for a successful sense-and-avoid platform was to determine what minimal information was necessary to resolve a particular conflict. In Figure 2, the four pieces of information used to classify a scenario are shown: aircraft location, speed, heading, and heading intersection point. In this figure, the triangular objects each represent a UAV and its location, the black arrows designate the heading of each aircraft and the magnitude of its current speed, and the red “x” depicts the location of the heading intersection point.
Figure 2. Conflict Scenario Classification Information
For each of the following figures, a series of aircraft pair scenarios have been shown. In each figure, one blue intruder aircraft is paired with one green “ownship”. Thus, the reader must not be confused by how many vehicles are shown in each figure. For example, in Figure 3 a), we can see two vehicles, and only consider these two vehicles when classifying the scenario. Therefore, each conflict pair is to be considered separately. That is, when considering the first conflict pair in a), the two aircraft that appear b) are to be ignored entirely.
a)
b)
c) Figure 3. Conflict Classification #1
d)
In the first conflict classification, shown in Figure 3, the intruder is either on the left of the ownship going to the left, or right of the ownship going to the right. In Figure 3 a) and b) the intruder is moving away from the aircraft, whereas in Figure 3 c) and d) the intruder is moving towards the aircraft (with respect to their relative heading). In each of these cases, the logic would determine the safest avoidance maneuver would be for the ownship to go behind the intruder.
a)
b)
c) d) Figure 4. Conflict Classification #2
e)
f)
In the next conflict classification scenario, shown in Figure 4, we are evaluating cases in which the intruder aircraft has a relative heading of either 0° or 180°. For these relative headings, there are three possible scenarios. The intruder can either be located to the left of, directly in front of, or to the right of the ownship. In Figure 4 a) and b), the intruder is located to the right of the ownship, and has a relative heading of 0° and 180°, respectively. To this point, every filtering technique has been used to determine whether an ownship should go in front of or behind an intruding aircraft. However, in these scenarios where the headings are exactly parallel these classifications do not physically make any sense. Therefore, in these cases the controller will not run either of the collision avoidance FISs, rather, it will decide whether the ownship should turn to the left or turn to the right. The heading rate for each of these turns was hard-coded to be one quarter of the maximum turn rate possibly achievable by the aircraft at its current operating speed.
With this new type of avoidance classification the logic will determine that the ownship should turn to the left to avoid the collision for the conflict classification #2 scenarios. In Figure 4 a) and b), the intruder is located to the right of the ownship, and thus should turn to the left to avoid the conflict. However, for the cases shown in Figure 4 c) through f), we see that the intruder is either on the left of or directly in front of the ownship. For each of these conflict scenarios, the logic will determine that the ownship should turn to the right to avoid the collision.
a)
b)
c) Figure 5. Conflict Classification #3
d)
In Figure 5, the scenarios where the ownship is approaching the intruder from behind is shown. In Figure 5 a) and b) the ownship is predicted to reach the heading intersection point before the intruder. Here, it can be seen that although the intruder is physically located closer to the heading intersection point, it is traveling at a slower speed. Therefore, when taking each UAV‟s velocity into account the ownship is predicted to reach that point in two seconds, whereas the intruder is predicted to reach that point in four seconds, as denoted by “T” in the figure. With this information, the ownship logic will determine that it should go in front of the intruder. When doing so, it is obvious that it will pass the intruder. When this happens, it will no longer detect the intruder, and the blue UAV will now need to avoid the green UAV. Once this occurs, the green UAV will be on its right, traveling to the right, which is described by the conflict classification scenario #1 so therefore resulting in a go behind maneuver. For the scenarios shown in Figure 5 c) and d), we see that the aircraft are in the same orientation as shown in conflict scenario a) and b); however, now the intruder is predicted to reach the heading intersection point prior to the ownship. In this case the ownship will determine that it should maneuver behind the intruding aircraft to avoid the collision. While not shown here, this same logic also holds when the two aircraft have an equivalent predicted time to the heading intersection point. In this scenario, the safest maneuver would again be for the ownship to go behind the intruding UAV.
a)
b)
c) Figure 6. Conflict Classification #4
d)
The only remaining aircraft orientation that needs to be explored to completely cover the solution space is when an intruder is moving towards the ownship. This aircraft orientation has been visualized in Figure 6. For this final orientation, we have three possible conflict scenarios that can occur. The ownship‟s projected time to the intersection point can be less than, equal to, or great than the intruder‟s projected time to the heading intersection point. In Figure 6 a) and b), we can see that the ownship is predicted to beat the intruder to the intersection point. Therefore, in this scenario, the ownship will be instructed to go in front of the intruder. However, Figure 6 c) and d) the case where the intruder is closer to the calculated intersection point is shown. Here, the system will instruct the ownship to go behind the intruder.
a)
b) Figure 7. Conflict Classification #5
Lastly, in Figure 7 the case in which both UAVs are predicted to reach the intersection point at the same time is shown. In this scenario, we will instruct the ownship to turn to the right. Therefore, regardless of which side the intruder is approaching the ownship, we will always turn to the right. The reasoning behind using this persistent logic is due to the fact that we must also consider the intruder UAV‟s perspective. Using this logic, both UAS will turn to the right, ensuring that the aircraft do not turn towards one another. If, on the other hand, we instructed the ownship to turn to the right when the intruder was on its right and to turn to the left when the intruder was on its left, we would run into issues where the two aircraft turn towards one another. For example, let‟s first assume the green UAV is the ownship in Figure 7 b). In this case, the ownship would turn to the right since the intruder is on its right. Now, let‟s assume we are onboard the blue UAV, making it the ownship. In this case, the green intruder is located on its left and therefore would turn to its left. This logic would therefore faulty and cause the two aircraft to turn towards one another. A pseudo-code describing the different encounter scenarios and their respective decisions is shown in Table 2. To properly follow the logic shown in this table, one must first evaluate the first row, then evaluate the second row next, and so on, similar to how it would be processed in the code. When reading the table in this way, every possible encounter scenario and decision is described. In this table, using the phrase “I‟m Closer” is referring to the aircraft having less time to reach the heading intersection point than its intruder, not which aircraft if physically closer to the heading intersection point. Furthermore, the first four columns in each row designate how the conflict is being described, and the final column shows the system‟s decision. Although not explicitly shown, the third column is an operator that connects the second and forth columns. Therefore, to make these statements complete, a set of parentheses should lie around the statements in both the second and fourth columns to show the order of operations for the operators AND and OR. Table 2. Summary of Collision Avoidance Filtering Logic
IF
On Right AND Going Right
OR
On Left AND Going Left
Go Behind
ElseIF ElseIF ElseIF ElseIF ElseIF ElseIF
Head on OR Trailing Head on OR Trailing I‟m Closer I‟m Farther OR Equidistant I‟m Closer Equidistant
AND AND AND AND AND AND
On Right On Left OR Straight Ahead Approaching from Behind Approaching from Behind Coming Towards Coming Towards
Turn Left Turn Right Go In Front Go Behind Go In Front Turn Right
ElseIF
I‟m Farther
AND
Coming Towards
Go Behind
2. Fuzzy Inference System With the filtering techniques described in the previous section it is clear that the system can select four different desired actions: go behind, go in front, turn right, or turn left. When either the go in front or go behind decision is selected, the collision avoidance system will activate the corresponding FIS to perform the desired maneuver. To develop the FISs used to maneuver the ownship in front of or behind the intruder, a three input one output method was used. Here, the FIS uses the separation distance between the two aircraft, their relative heading, and the aircraft closure rate as inputs to determine the appropriate turn rate in the correct direction. Due to the fact that we are analyzing a heterogeneous system, each aircraft platform can operate at different cruising speeds. Thus, the FIS
must be robust enough to ensure that a proportional amount of turn rate is used to avoid a collision. Therefore, by taking into account the conflict pair closure rate, we can achieve two things. First, we can ensure that we are not expelling more energy than necessary, that is, if two aircraft have a fairly low closure rate the FIS will not need to use a full effort turn to avoid the conflict. Not only does this save energy and fuel in the system, but it also helps limit the total deviation from the desired flight path. Second, we can ensure that regardless of the closure rate, the system will have the robustness to turn sufficiently to avoid the collision.
Figure 8. New Collision Avoidance FIS
In Figure 8, the structure of the collision avoidance FIS is shown. For each input and output, the number of membership functions and the corresponding classification can be seen. While the structure of both the go behind and the go in front FISs are the same, they each use different rule sets. In these systems, the rules are exactly opposite one another so when one would determine the aircraft should turn right, the other would turn left, and vice versa. To better understand the workings and decision making process of each collision avoidance FIS, the various inputs, output, and corresponding membership functions have been shown in Figure 9. It is important to note that the various combinations of inputs are all connected using AND statements. When this AND connector is used, the FIS will determine which input is most critical by determining which has a minimum membership value. closeR 1
0.8
Degree of Membership
Degree of Membership
Close 1
0.6
0.4
0.2
0
0.8
0.6
0.4
0.2
0 60
80
100
120
140
160
180
0
10
20
Distance
a) Input 1
40
50
60
b) Input 2
L 1
R
0.8
R 1
Degree of Membership
Degree of Membership
30
Closure Rate
0.6
0.4
0.2
0
L
0.8
0.6
0.4
0.2
0 0
50
c) Input 3
100
150
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350
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
d) Output Figure 9. Collision Avoidance FIS – Input Membership Functions
0.4
0.6
0.8
1
By referencing Figure 9 a) the lower membership degree for the set distance, is at its upper bound, that is, its maximum sense range. Similarly, in Figure 9 b), the lower degree of membership is assigned to a lower closure rate. Due to using the AND connector between these inputs, a maximum aggregation technique, and a mean of maximum defuzzification interface, whichever membership value is lowest, the output heading contribution will similarly be low. Therefore, if a closure rate is low, the output heading rate will also be low, and similarly if the distance if large the output heading rate will be low. However, as the distance decreases or closure rate increases, the heading rate output will also increase. This ensures that the aircraft does not waste energy unnecessarily in less extreme conditions. In Figure 9 c), the membership functions for the relative heading input are shown. This input determines whether the aircraft should turn to its right or left to avoid the collision. Lastly, Figure 9 d) shows the heading rate output and its corresponding membership functions. B. UAV Traffic Management System 1. Vehicle Priority The vehicle priority algorithm used in this updated UTM system varies from the one presented for the preliminary UTM testing. It is important to note that this algorithm is only used for separation assurance techniques. Seeing as it doesn‟t make much sense for both aircraft to deviate from their flight plans to prevent a possible LOS, one vehicle will be given a higher priority. To determine which aircraft has a lower priority, and therefore must perform a deviation from flight path to avoid a loss of separation, a series of evaluations are made. First, the UTM system will check to see if the two aircraft are heading in a similar direction, meaning their relative heading is within 5° of one another. These encounter scenarios can be seen below in Figure 10.
Figure 10. Encounter Example with Relative Heading Within 10°
If a scenario like this occurs, the aircraft that is trailing the other aircraft will have the lower priority. Therefore, if several aircraft are in line, traveling in the same direction, the aircraft in the back will have the lowest priority, and must avoid all other aircraft in its given proximity. Whereas, the UAV in the front of the pack will have the highest priority, thus continuing on its desired flight path without any deviation or regard for the other aircraft. If the aircraft are not in the scenario described above, the UTM system will give priority to the aircraft that is closest to its next target. With this series of checks, this algorithm will determine which UAV(s) will deviate from their flight path when a possible LOS has been predicted using only current state information. 2. Separation Assurance In this study, the term “separation assurance” refers to a preemptive action to ensure aircraft do not lose “wellclear” standards. Due to the scope and autonomy of the system used for this research, our definition of well-clear will most likely be much smaller than what we see as the standard for the early stages of UTM. Overall, we imagine that the FAA will set the standard for a loss of well clear to occur somewhere between 0.8-1.2 nmi. This horizontal separation standard seems to be large due to how small the closure rates of small UAS in class G airspace are, but these standards will also have to adhere to manned aircraft in the near future, seeing as the UAS will be operating in uncontrolled airspace. As the level of autonomy and technology increase over the coming years, the definition of well-clear will likely reduce. Despite these advancements, it is unlikely that the separation standard will fall below 0.8 nmi. Due to MTOW restrictions on small UAVs, the amount of sensors and hardware on-board a UAS are limited. Therefore, we can assume that each aircraft will have object detection and ADS-B out capabilities. The ultimate reason why we want to push the limits of the definition of “well-clear” is to see how UAS will operate when systems are capable of partial autonomy (or full autonomy with on the loop human monitoring). Therefore, a loss of well-clear is defined to be less than 0.1 nmi. If two aircraft become closer than this distance, a RA would be sent to the UAS to perform a collision avoidance maneuver. In order to prevent the aircraft from having a LOS (i.e. loss of well-clear), a separation assurance algorithm was developed using Fuzzy Logic. For a simplistic approach, similar techniques that were used for the collision avoidance were used for the separation assurance algorithm. The major differences between these two systems are the horizontal range at which the maneuver is executed and priority will always be given to one of the aircraft. Another aspect that we wish to incorporate in this algorithm is the estimated time to LOS. Therefore, there will be two thresholds that must be broken for the separation assurance maneuver to be enabled: distance between aircraft and predicted time to LOS. If at any point in time two aircraft are within 0.4 nmi AND within two minutes
of predicted LOS, the system will become active and a new flight trajectory will be calculated. The reasoning behind using both of these standards is simple. We not only want to ensure safe operation, but we also want to limit the number of flight adjustments that are imposed on each aircraft. If these constraints are too large, aircraft may perform unnecessary maneuvers to prevent a loss of well-clear, but in reality, since the intent of the other aircraft(s) are unknown, this prediction is by no means a certainty. Therefore, we want to reduce the constraints far enough so that aircraft do not repetitively perform unnecessary adjustments, but large enough to still ensure safe operation. To predict whether a LOS will occur once aircraft are within 0.4 nmi of one another, the UTM system will use the current state information of each aircraft to project their flight paths into the future. Using the current bearing, velocity, and position of each aircraft, this projection will show what the closest point of approach between the aircraft would be if they remained on their current headings. Using this information, the system can check if the CPA will result in a loss of well clear, and whether or not it will occur within two minutes into the future. If both thresholds are met, the separation assurance methods will be enacted. Similar to the collision avoidance platform, a series of filtering techniques were used to classify various encounter scenarios, and decide which action should be taken to avoid a potential loss of separation. While the overall methodology and structure is identical to the collision avoidance logic, there are several changes and additions to account for due to the fact that only one vehicle will deviate from its flight path to avoid the loss of well clear. In this system, the parameters used to classify a conflict scenario are identical. In addition, each FIS structure is the same as described in the collision avoidance, with the exception of the “Distance” membership function. While it has the same shape as previously shown in Figure 9 a), it has different upper and lower bounds to account for a larger detection range by the UTM system.
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b) c) Figure 11. Separation Assurance Conflict Scenario Classification
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Figure 11 depicts a case where the ownship would determine it was safe to go in front of its intruder. When the ownship is approaching from behind the intruder, as seen in Figure 11 a) and b), It can be seen that the UAV will only make this decision if it is at least 30 seconds closer to the intersection point. On the other hand, if an aircraft is coming towards the ownship, as seen in Figure 11 c) and d), the aircraft will only go in front of its intruder if it will reach the intersection point at least five seconds prior than its intruder. A summary of all filtering cases for the UTM separation assurance system can be found in Table 3. Table 3. Summary of Separation Assurance Filtering Logic
IF ElseIF ElseIF ElseIF ElseIF
Trailing On Right AND Going Right Head on OR Trailing Head on OR Trailing I‟m 30+ Seconds Closer
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Relative Heading 5° On Left AND Going Left On Right On Left OR Straight Ahead Approaching from Behind
Go Behind Go Behind Turn Left Turn Right Go In Front
ElseIF ElseIF ElseIF
I‟m NOT 30+ Seconds Closer I‟m 5+ Seconds Closer I‟m NOT 5+ Seconds Farther
AND AND AND
Approaching from Behind Coming Towards Coming Towards
Go Behind Go In Front Go Behind
With the above logic, the UTM system will make a decision to which direction each vehicle should turn so that it may successfully avoid a loss of well clear. In each of these scenarios, one final check is used to ensure that the aircraft does not prematurely turn back towards their desired waypoint prior to completing the avoidance maneuver.
In other words, as soon as the intruder aircraft has vacated the ownship‟s field of view (i.e. 180° in front of the aircraft) the aircraft will naturally want to continue on its mission since it can no longer sense the intruder. In these circumstances, the UTM system will check to see if at that point in time the aircraft turned back towards its desired target another predicted LOS would occur. If this predicted LOS was found, then the avoiding aircraft will continue on its current heading until it is cleared to turn back towards its desired target.
a) Time 1 b) Time 2 c) Time 3 Figure 12. Full Separation Assurance Maneuver
In Figure 12, an example of a full separation assurance maneuver has been depicted. In this figure, each aircraft and their respective heading have been identified, as well as the desired target of the green UAV. In Figure 12 a), the two aircraft have been predicted to have a LOS, so a RA has been uploaded to the green UAV. Once uploaded, it will turn to its right to prevent the LOS. Then, in Figure 12 b), let us assume that the green UAV has successfully prevented the LOS. At this point in time, the UTM system will now check to see if it is safe for it to return to its assigned mission, that is, to turn back towards its desired waypoint. By referencing this figure, it is clear that if the UAV turned back at this moment, another predicted LOS between the two aircraft would occur. Therefore, it will continue on its current heading until it is safe to turn back towards its desired target. Finally, in Figure 12 c), the UTM system has determined that it is now safe for the green UAV to continue on its mission, thus the aircraft turns back towards its desired waypoint. To test the effectiveness of these mitigation techniques, two trials on the final simulation platform will be conducted. First, the system will be simulated without the use of the separation assurance mitigations. Then, the same simulation will be re-evaluated with these features enabled. In each case, the system will track the number of RAs sent to the aircraft (i.e. how many loss of well-clear violations occurred) so that a direct comparison can be made. C. Overarching Control Logic To help understand how each subsystem is integrated into the overarching logic, a flow diagram has been created. In this flow diagram, the various steps for determining which action should be taken at any given time are presented. This flow diagram can be found in Figure 13. By referencing Figure 13, one can see how the code determines whether the UTM system should be activated, a collision avoidance maneuver should be activated, or whether no action should be taken. On the first iteration of the program, the separation distance between all aircraft pairs are found. With this information, a future time to check the separation between each aircraft pair is set. Once the future time to re-check the separation between two aircraft is met, the code will see if the two aircraft are within 0.4 nmi of one another. If they are not within 0.4 nmi, another future time to check their separation is calculated and set, however, if they are separated less than 0.4 nmi we now need to check to see if a loss of separation has occurred (i.e. within 0.1 nmi of one another). If this is the case, the collision avoidance system will be enabled. However, if a LOS has not yet occurred, we now need to check to see if the UTM system should be enabled. The two criteria for checking to see if we need to activate the UTM system is if the predicted CPA (pCPA) creates a LOS AND if the time to LOS (tLOS) is within two minutes. If both criteria are met, we now need to check to see if the aircraft has already began performing a separation assurance maneuver. If it has not already been assigned to perform a deviation from its desired flight path, a resolution advisory will be sent from the UTM ground station to the UAV to prevent the LOS. However, if the UAV was already assigned a maneuver, prior to turning back towards its original desired waypoint, it much check to see if doing so will cause another predicted LOS. If it is safe to turn back it will do so, however, if it is not safe to turn back, it will continue on its current heading.
Figure 13. Logic Flow Diagram
If it was found that the pCPA and the tLOS criteria were not met, no separation assurance maneuver is required. Therefore, no action is taken, and the aircraft will continue towards their desired waypoints. Once the final decision by the system has been made, the system will re-assign a future time at which that particular aircraft pair separation should be checked. This ensures that if the aircraft are within 0.4 nmi of one another, this logic system will run continuously.
V. Controller Testing Prior to integrating the controllers presented in the previous section into the full simulation environment, testing was conducted to ensure each was operating as desired. In this section, the methods used to test each controller are described. In addition, the results of this testing have been shown and analyzed. A. Separation Assurance To analyze the effectiveness of the collision avoidance system, a testing platform was created to simulate a large number of encounter scenarios. The purpose of this testing platform was to evaluate as many encounter scenarios between a pair of UAVs as possible, analyze the effectiveness of the separation assurance platform, and identify where the controller was unsuccessful at preventing a loss of separation. In order to do this, we wanted to test the full field of relative heading angles and separation distances to the intruding UAV. Therefore, starting from a relative heading of 10°, where the intruder is nearly traveling in the same direction but moving from right to left, the two aircraft are forced to be in conflict. Once this encounter scenario trial was complete, the simulation would reset the two aircraft to a new initial relative heading. This process was repeated many times. With successive iterations, a small angle is added to the relative heading between the two aircraft until a relative heading of 350° is reached.
Once the aircraft pair had completed the evaluation for the full spectrum of relative heading angles, the full simulation was re-evaluated for eight more test cases. For each new test the intruder aircraft starting position was changed. Originally, both UAVs were equidistant from the intersection point. On the next test case, the intruder is slightly closer to the intersection point for the entire spectrum of relative headings. A large number of initial positions were evaluated for an intruder closer to, and farther from, the intersection point. An example of a full case of test scenarios can be seen in Figure 14. In this figure, the black triangle represents one UAV‟s starting location (same for every trial), each small red circle represents a starting position of the intruder, and the “x” located at the center of the figure signifies the point to which each aircraft is originally moving towards. Therefore, each concentric ring represents a full spectrum of initial intruder angles, and each ring alters the distance to the heading intersection point. Initial Goal Intruder Self
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Figure 14. Example of Encounter Scenarios
For each encounter scenario that was tested the CPA was logged for each intruder angle and starting distance. An example of how the CPA results are displayed for all encounter scenarios can be seen in Figure 15.
Figure 15. CPA vs. Intruder Angle Example
In Figure 15 we have shown the results from one of the concentric rings of intruder locations shown in Figure 14. Here, the CPA versus intruder angle was plotted on a polar graph where the CPA and the intruder angle are denoted by „CPA‟ and „ ‟, respectively. Therefore, each blue „x‟ in this figure represents the closest point of approach for that particular initial intruder angle. In addition, the small red circle around the center of the plot has a radius of 60 meters and represents the “collision zone” between the two aircraft. Therefore, if any of the CPA data points lie inside of this circle, the test point is considered to be a collision.
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Figure 16. Separation Assurance Test Cases
During this testing a large spectrum of aircraft pair geometries were analyzed. Here, we have tested 680 intruder angles for nine different initial intruder distances. The full spectrum of aircraft pair starting locations can be seen in Figure 16. Since the full simulation platform uses a heterogeneous set of agents, there were four full testing scenarios that were evaluated: fixed vs. fixed, multi-rotor vs. multi-rotor, fixed vs. multi-rotor, and multi-rotor vs. fixed. For the first heterogeneous case the fixed wing vehicle starting location was held consistent, while the multirotor changed its initial starting location on each successive iteration. Conversely, in the multi-rotor vs. fixed case, the multi-rotor vehicle starting location is the same for each iteration. 200
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In the figures 17 through 19, the resulting CPA versus the starting intruder angle is shown. In Figure 17, the CPA was plotted on a polar graph, similar to what is shown in Figure 15, for every test point shown in Figure 16. In each of these plots, the center point is depicted by a black dot at (0, 0). This represents a CPA of zero between the two aircraft. The distance away from the center point, indicated by the solid blue line, represents the closet point of approach. The angle formed between the center point and the CPA point represents the initial intruder angle for that encounter scenario. The red circle around the center point has a radius of 60 meters, this represents the collision
radius. Lastly, the black circle represents the loss of separation radius. If any of the CPA data points go inside of this black circle, that particular encounter pair was considered to have had a LOS. The results shown in Figure 17 depict the scenario where both aircraft are multi-rotor vehicles. Each figure going from a) to i) represents a new constant initial intruder distance from the center point; where a) is the inner most ring and i) is the outer most ring in Figure 16. In every encounter scenario tested for the multi-rotor vs. multi-rotor case, no losses of separation occurred. However, the results do produce some unique results seeing as the CPA distribution isn‟t smooth or uniform based on the intruder angle. It can be seen from these cases that not every instance was 100% successful at preventing a LOS. In every figure from c) to i) at least one LOS occurred, as indicated by the small red dot on the figure. Due to the homogeneous fixed-wing case being very similar in nature, it has been presented in this paper. The only major for the fixed-wing case was that in every encounter scenario tested, no losses of separation occurred. In Figure 18 the results of the first heterogeneous case are shown. Here, the fixed wing platform was held consistent at the starting location shown in Figure 16. As opposed to the irregular results shown in both homogeneous cases, here we see smooth transitions for incremental increases in the intruder angle. Furthermore, the general shape of each test was fairly similar. While the CPA for each homogeneous case produced results very close to the LOS circle, here we can see that the separation assurance maneuver is much more reliable with higher separation tolerances, minus the exception of the head on cases. In each nearly head on case, several losses of separation occurred. 1500
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To have a better understanding of where these failures lie, a zoomed view of each encounter scenario shown in Figure 18 are shown in Figure 19. In this zoomed view, each case consistently resolves the conflict when the intruder is nearly head on. However, if the angle becomes slightly too large, a LOS will occur. Then once this LOS occurs, the CPA gradually becomes larger until the resolution is again successful at preventing this loss of well clear.
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Figure 19. Separation Assurance CPA vs. Intruder Angle - Heterogeneous, Fixed Same, Zoomed View
The results for heterogeneous case where the multi-rotor aircraft was held in the same position has been omitted from this paper due to the fact that the results were nearly identical to the results shown in Figure 18 and Figure 19. B. Collision Avoidance The collision avoidance software was tested in the same manner to how the separation assurance testing was conducted. A large number of aircraft encounter pairs were set up and tested. For each encounter scenario the CPA versus the initial intruder angle was tracked. The full spectrum of test points can be seen in Figure 20. Initial Goal Intruder Self
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Figure 20. Collision Avoidance Test Cases
In this figure, the initial position of the aircraft held at a constant position throughout all testing is represented by the black triangle. In addition, each small red circle depicts one of the many intruder starting locations. In each test case, both aircraft have an initial heading pointing towards the small “x” located at the center of the figure. In the following figures, the CPA for every encounter scenario shown in Figure 20 is depicted. In each figure, the black dot at the center of each plot represents a closest point of approach of zero. The CPA in each figure is depicted by the blue line, where the CPA is measured from the blue line to the center point at (0, 0). The angle formed between the center point and the CPA point represents the initial intruder angle for that encounter pair
scenario. Lastly, the red circle around the center point has a radius of 60 meters, this represents the collision radius. Therefore, if any of the CPA data points lie inside of this red circle the two aircraft were considered to have collided. 500
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In Figure 21, the closest point of approach versus the intruder angle is shown for when both aircraft are fixed wing vehicles. Figure 21 a) represents the case in which the intruder is closest to the center point, that is, the inner most concentric ring in Figure 20. Whereas, Figure 21 i) depicts the cases in which the intruder is farthest from the center point, or the outermost concentric ring in Figure 20. It is clear that no CPA points lie inside of the collision radius for every intruder distance, and therefore, the collision avoidance algorithm was 100% effective at preventing each potential collision. Although some slight jumps in the CPA for a small change in the intruder angle can be found, this most likely is a result of the logic changing between two consecutive encounter scenarios. For example, if at one intruder angle the aircraft could have both turned to the right to avoid one another, whereas in the next they both turned to the left. Or in other words, the aircraft that decided it should go behind in the first case, decided it should go in front in the next case. Again, for the sake of brevity, the homogeneous multi-rotor case has been omitted from this discussion. The results were quite similar to the results shown in Figure 21. In both cases, the closest CPA for each intruder location was consistently larger than 90 meters. However, for the multi-rotor vehicles, we found the CPA larger for each test case since the aircraft are traveling at a slower speed; therefore, this results in a lower closure rate and thus the vehicles have more time to react and avoid one another. The remaining scenarios that need to be tested are for the mixed aircraft platforms. In Figure 22, the case where the fixed wing has the same starting location for all conflict pairs is shown. When comparing the results of this plot to each homogeneous case, it can be seen that the general shapes of each polar plot are quite different. In this heterogeneous case, the general shape of each case is fairly consistent, excluding the case shown in Figure 22 a), which has some unique features in quadrant I of the plot. It is also worth mentioning that the cases where the multirotor aircraft is farther away from the center point than the fixed wing vehicle, as shown in Figure 22 f) through i), the CPA is quite larger than the cases where the initial separation between aircraft are equidistant or closer to the center point. This makes sense, due to the fact that the fixed wing aircraft is traveling faster. Since the multi-rotor essentially starts behind the fixed wing, it will never approach the fixed wing. The final case where the multi-rotor vehicle starts at the same location each time is shown in Figure 23. Again, no collisions occur for any of the test points. As opposed to the results shown in Figure 22, the cases in which the intruder is closest to the center point results in the highest CPA values. This again is due to the fact that the fixed wing is located in front of the multi-rotor. Again, the general shape of each plot is fairly similar. Lastly, the minimum CPAs all lay near the head on encounters as expected.
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From this collision avoidance testing, it is clear that the system is fairly robust and is successful 100% of the time. Therefore, when implemented into the full simulation platform, we would expect very few collisions to occur when only two aircraft are in conflict. This testing is again not exhaustive, many encounters may occur in the full simulation that was not tested here. In addition, no cases where three aircraft were in conflict were tested.
VI. Results A. Simulation Platform An example of a full testing platform when all missions have been assigned can be seen in Figure 24. While the agriculture, roadway, and forest applications necessarily need to complete their missions in their respective areas, the disaster management and delivery services can be assigned waypoints anywhere within the airspace. In the roadway surveillance missions, two UAVs have been assigned to every road, starting at each end of the road traveling towards one another. Therefore, it is expected that the aircraft pair will have to avoid one another at some point in their mission. By referencing Figure 24 it can be seen that the traffic density can be quite high in the concentrated agriculture and forestry areas. Therefore, it is expected that several conflicts will arise throughout the simulation that must be solved by either the UTM system or the on-board SAA technology. At the top of the figure, the total number of active missions and active UAS are shown. Within this platform, two different simulations were tested. First, a baseline case was tested where the UTM system separation assurance capabilities were disabled. Using this baseline case, we could assess the true level of air traffic density in this airspace by seeing how many losses of separation occurred per flight hour. Once this baseline case was completed, the UTM system was enabled to see how the additional level of safety would mitigate the risk for a loss of separation. Number of Active Missions:14
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B. Separation Assurance – Disabled For the results presented in this section, the separation assurance capabilities of the UTM system were disabled. By doing so, we were able to track how many losses of separation occurred throughout testing. The results of this study have been shown in Table 4. Table 4. Results for Simulation with Separation Assurance Disabled
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In this table it can be seen that over the span of 8,710 flight hours, on 18,179 occasions aircraft came within 0.4 nmi of one another. In these instances, if the UTM system was enabled, it would have checked if these two aircraft were predicted to a have a loss of separation within the next two minutes. If the predicted CPA was less than 0.1 nmi the separation assurance system would have uploaded an RA to the vehicle to prevent the LOS from occurring. Of the 18,179 times that the aircraft came within 0.4 nmi, 3,528 losses of separation occurred. In all but 9 of the instances where a LOS occurred, the collision avoidance software was deployed. In these 3,519 encounters, the collision avoidance software was 99.97% successful. From these results, we expect that when the simulation is run with the separation assurance capabilities turned on there will be a comparable number of separation assurance maneuvers that will be used to prevent these losses of separation from occurring. While this is true, we expect that some of these maneuvers will not be successful at preventing a loss of separation based on the results from the separation assurance testing. C. Separation Assurance – Enabled Once the baseline simulation was tested, the features of the UTM system were enabled to allow for separation assurance between aircraft. With this additional mitigation in place, it was expected that the number of LOS would drastically decrease. However, it was still expected that some LOS would occur due to the separation assurance algorithm not being 100% effective, especially in head on encounters between a heterogeneous pair of aircraft. Table 5. Separation Assurance Results
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Table 6. Collision Avoidance Results
Collision Avoidance Maneuvers
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In Table 5, the results of the separation assurance platform are shown. While the number of missions completed between the tests where this service was disabled was equal, this simulation resulted in 7,545 flight hours. Over this span of time, on 15,178 occasions aircraft came within 0.4 nmi of one another. However, of those instances the UTM system predicted a LOS to occur within two minutes only 5,412 times, therefore enabling a separation assurance maneuver to the vehicle with less priority. Of these separation assurance maneuvers, only 84.77% were successful, resulting in 824 losses of separation and losses of separation per flight hour. On every occasion that a LOS occurred, the SAA software calculated a maneuver to avoid the potential collision between vehicles. The results of the collision avoidance software can be seen in Table 6. In these instances, it was found that the collision avoidance software was successful at resolving a conflict 99.51% of the time in 823 maneuvers, resulting in collisions per flight hour. To obtain a better understanding to why each of the five collisions occurred throughout testing, the trajectories of each encounter were stored in an external file so that post-processing could be used to visualize these failed conflict resolutions. A few examples of these trajectories have been plotted over time and can be seen below. 300
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b) Figure 25. Collision Example #1
In the first collision scenario shown in Figure 25, we can see that the two aircraft in conflict are of different vehicle types. In Figure 25 a), the blue aircraft is a fixed wing vehicle and the red aircraft is a multi-rotor vehicle. This can be verified by referencing Figure 25 b). In this figure it is clear that the aircraft originally located near the
point (150, 200) is a fixed wing aircraft, and the other vehicle is a multi-rotor aircraft due to the fact that the fixed wing is traveling faster, and therefore, its data points are more spread out than the multi-rotor. In this conflict scenario, the fixed wing aircraft obtains its desired waypoint, and then attempts to turn to its left to capture its next waypoint. At this time, it recognizes that a potential loss of separation is about to occur, so it decides it should adjust its heading to go behind the multi-rotor vehicle. The fixed wing UAV then continues on this heading until it is safe to turn back towards its desired waypoint. The fixed wing then captures its next waypoint near (-200, -100), therefore being assigned to another new waypoint. Due to the fixed wing initially adjusting its heading to turn to its right, it can be assumed that its next waypoint is in that direction. However, as soon as it attempts to do so, it sees the multi-rotor aircraft in its field of view. At this point in time, the fixed wing aircraft again attempts to perform a separation assurance maneuver to go in front of the multi-rotor UAV. Although this may eventually resolve the conflict, the fixed wing aircraft inevitably still wants to visit its desired waypoint. Therefore, once the potential LOS has been resolved, the aircraft turns to its right to head towards its next waypoint. By doing so, another potential LOS is found. This process of attempting to turn back towards the next waypoint and resolving the conflict repeats itself until the fixed wing catches up to the multi-rotor. At this point in time, the collision avoidance software is enabled, but is too late to solve the conflict. The multi-rotor UAV now decides that it should go behind the fixed wing, and vice versa. However, since the fixed wing aircraft‟s field of view is only 180°, as soon as it turns far enough to the left to go in front of the multirotor, it can no longer sense the intruder, causing it to turn to its right to go back towards it desired waypoint. Once it turns back, the conflict is again recognized. This oscillation continues until the two aircraft become to close, and a collision is detected. 150
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b) Figure 26. Collision Example #2
In this second collision scenario, shown in Figure 26 a), it looks as if only the two aircraft are in conflict. However, when zoomed out to see the full surroundings as shown in b), it is found that a third aircraft is within each vehicle‟s sensing range. Since the UAS near the bottom of the plot does not deviate from its flight path, it is assumed to have priority over the others. Therefore, the top UAS, as depicted in blue in Figure 26 a), attempts to go behind both of its intruders. However, the center UAV attempts to also go behind the intruder with the highest priority, thus also turning to its left. In this scenario, the blue UAV at the top of the encounter turns harder to its left than the red UAV directly below it, causing a collision to occur. Of these two scenarios described above, at least one can describe where the other failures occurred in the remaining three collision cases. Therefore, either the collision avoidance software created a scenario were the two aircraft would continue in a similar direction until they become too close to one another or three or more aircraft in conflict caused a collision. For the sake of brevity, the other collision cases have been omitted from this discussion.
VII. Conclusion In this study, a fuzzy logic approach was used to help mitigate the risk of a collision between two UAVs in congested low altitude airspace. This airspace was modeled using a 3-dimensional environment encompassing several different vehicle platforms, each with unique performance characteristics. By integrating heterogeneous agents into the airspace, we were able to test these controllers for different encounter scenarios with several different closure rates between aircraft. To help increase the level of safety of the system, two controllers were developed. First, a robust sense and avoid system was developed to ensure that two aircraft within close proximity of one another could successful resolve a potential collision using only current state information and without communicating with one another, that is, without performing a coordinated maneuver. This collision avoidance logic proved to be successful throughout testing for both the homogenous and heterogeneous cases.
Next, to further mitigate the risk of a potential collision, a separation assurance platform was developed. The goal of this platform was to predict if a loss of separation between two aircraft would occur within a given lookahead time. If a loss of separation were to occur, the UTM system would determine which aircraft would perform an adjustment to its current flight path to ensure this loss of separation did not occur. Within this simulation, it has been assumed that the UTM system has global knowledge of every aircraft within the airspace. If aircraft state data were for some reason not available to the UTM system, it UTM mitigations would not be used, and thus each agent would have to solely rely on its ability to sense and avoid intruders. To test the effectiveness of these mitigation techniques in a realistic environment, the simulation was run continuously until the number of total missions completed by the UAS was equal to a user defined value. Using this methodology, two cases were evaluated, one where the UTM system was disabled, and a second where the UTM mitigations were in place. In each of these simulations, the same set of missions and waypoints were visited. Therefore, the case where the UTM system was disabled served as the baseline case. With this baseline case the effectiveness of the UTM separation assurance techniques could be analyzed. The results of the baseline simulation case are shown in Table 4. Overall, the results of this experiment were as expected. Throughout simulation a total of 8,710 flight hours were recorded. During this time, 3,528 losses of separation occurred, or in other words one loss of separation occurred for roughly every two flight hours. This number of LOS per flight hour was considered to be quite high, thus showing that this simulation environment in fact models a highly congested airspace. Out of all of these losses of separation, 3,519 collision avoidance maneuvers were used by the onboard sense and avoid systems. Of these maneuvers, one collision did in fact occur. Therefore, the collision avoidance system was deemed to be 99.97% successful at resolving its conflicts. Overall, on average there were collisions per flight hour. In Table 5 the results of the final study where the UTM system mitigations were enabled have been shown. Here, over a span of 7,545 flight hours only 824 losses of separation occurred. Therefore, on average, the UTM system reduced the number of losses of separation from approximately one LOS per two flight hours, to one LOS per ten flight hours. Therefore, it is clear that the separation assurance techniques were successful at increasing the level of safety of the system. Although these results are favorable, the separation assurance procedures were used on 5,412 occasions, and therefore, only successfully solved a potential loss of separation 84.77% of the time. While the concept of using fuzzy logic to provide a means for preventing a LOS between aircraft has been shown, several additions should be explored to increase its reliability before implementing these practices into a physical system. For the scope of this research it is clear that the fuzzy system has drastically increased the overall safety of the system, and thus has been deemed successful. A final evaluation was conducted to compare the success rate and number of collisions per flight hour to the unmitigated case. In Table 6, the results of the collision avoidance system were shown for the case where the separation assurance platform was enabled. In this table, it can be seen that out of the 823 collision avoidance maneuvers, four collision occurred, thus deeming the collision avoidance software to being 99.51% successful. Overall, a total of collisions occurred per flight hour. These results show that the collision avoidance software performed marginally worse than the previously tested case were separation assurance was disabled. This reduction in performance, however, does not indicate that the separation assurance system caused more collisions to occur. On the contrary, the separation assurance system placed aircraft in scenarios that were not previously tested by the collision avoidance system. Examples of these instances where the collision avoidance system failed were presented earlier. For the majority of the cases, the collision occurred due to the separation assurance maneuver causing aircraft to travel parallel to one another. This type of encounter scenario was not tested in the collision avoidance development, and thus was not recognized during testing. While these results show that the system developed in this work is not perfect, it is important to note that no exhaustive tuning or optimization was used. Given that the aircraft only use current state information to resolve each conflict, these results are quite promising. These results show that it is possible to create a UTM system based heavily on Fuzzy Logic algorithms that can perform well in complex situations while requiring relatively few, simple inputs. Overall, this research shows that Fuzzy Logic enables a heterogeneous multi-agent system to collaborate effectively and autonomously in a real-time, dynamic, uncertain, and realistic environment. While the results presented in this study are encouraging, and serve as a great starting point for developing a reliable intelligent system for UTM, there are several items that still need to be addressed before this could be feasibly implemented into a physical system. Since the development of this system is still in its early stages, several assumptions were made to simplify the problem so we could test these techniques on a simplified baseline case. One major assumption of this work is that each agent was assumed to have perfect information (i.e. no sensor uncertainty) when making decisions. In a real-world environment, the information provided to each aircraft would have uncertainty, and in many instances, not all of the information may be available to the aircraft or the UTM
system. Furthermore, we have assumed that the UAVs are all operating independently, with no manned aircraft in the airspace. In a realistic environment, we would need to address the interoperability between UAVs and manned VFR, or IFR, traffic. Moving forward, there are several other areas for future work to be explored. Due to the fact that the FISs shown were hand-tuned, and a first attempt at demonstrating this as a valid approach, no optimization has been performed. However, there are several methods to help train each FIS to produce more optimal results. Genetic Algorithms (GAs), in particular, have been used with great success to tune both membership sets and rule bases. It is likely that the system developed in this study would have better performance after utilizing such training methods. Expanding the Fuzzy avoidance system is one possible area for future work. By including more inputs, such as relative velocity or change in relative heading, the capability of the system would be enhanced. Furthermore, several changes could be made to the filtering logic to ensure all possible aircraft encounter scenarios can be solved. For example, in the separation assurance platform a larger buffer should be used when determining when it is safe for an aircraft to maneuver in front of its intruder. Seeing as this was one of primary causes for each collision found throughout testing, changes such as this would help improve the reliability of the system. To increase the robustness of the filtering criteria, a cascading FIS structure could be used to determine the final action for resolving a conflict. Another addition to the Fuzzy avoidance system that could be added is the ability to perform altitude maneuvers. While it is more efficient for aircraft to perform lateral deviations to avoid a conflict, there may be some situations where an altitude maneuver is required. This type of situation could arise if the congestion at a particular altitude is too dense to completely resolve a conflict. While adding this capability could help improve the safety of the system in high density traffic areas, it also adds a significant level of complexity to the problem. To enable these maneuvers, additional conflict classification filtering techniques would need to be explored. Another layer of the avoidance software could be developed to check that the decision created a valid solution. By checking the decision using a “safety net” FIS, any conflicts that are left unresolved would throw up a flag in the system. For example, if for some reason two aircraft were instructed to turn towards one another in a complicated encounter scenario, the safety net FIS would identify this mistake, and propose a new solution to resolve the conflict. Adding this secondary level of defense would help increase the safety of the system and add redundancy to the system. This approach would again use a cascading logic structure. First, the collision avoidance software would propose a solution. Then the safety net FIS would check for any conflicts that have yet to be resolved. Finally, the collision avoidance FIS would take this new information into account when proposing a new solution. A final addition that would need to be used to help improve the performance of the collision avoidance platform when implemented into a physical system would be using various “buffers” for some of the filtering techniques. For example, when two aircraft are said to be equidistant from their heading intersections points, the filtering logic will tell each aircraft to turn to the right. While this does solve potential collision, in a real-world system each aircraft will not be supplied with perfect information. Therefore, a buffer should be applied so that each aircraft will turn to the right if the calculated time to the intersection point is within 15 seconds of one another. This will ensure that even when uncertainty in the system is introduced, both aircraft will make the same decision. Aside from the various changes that could be made to the avoidance fuzzy system, each avoidance platform needs to be tested for conflicts between three or more aircraft. In this study, both the collision avoidance software and the UTM platform have been tested for pairs of aircraft in conflict. Therefore, it is important to develop a testing platform that can test three or more aircraft in a large number of test cases. One proposed approach for testing these encounter scenarios would be to have a similar setup used for the two aircraft encounters. For each of the previously described test points, a third aircraft could be added that would also vary its initial starting position. Therefore, for every testing point that has been presented in this work, we would need to test a full range of a third aircraft intruder angle and starting location. The number of trials needed to conduct a full series of encounter scenarios is exponential in size. That is, as the number of vehicles being tested increases, the number of cases increases exponentially. While this in practice can be achieved, the overall simulation time will greatly increase. For such scenarios where tuning would be used to help improve the performance of the system, this full spectrum of encounter scenarios would have to be run several times before the tuning is complete, thus further increasing the computational time. Due to this area of research being a fairly new topic, and UTM as a whole being in its infancy, a benchmark for developing such safety critical systems has not yet been established. Therefore, in the near future a standard for operating small UAS in uncontrolled airspace needs to be set. With this baseline, the performance of the techniques proposed in this study could be compared to a current state-of-the-art system. This would allow for the testing and direct comparison of many approaches, controllers, and methodologies to solving such a large scale decision making problem.
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