Hierarchical Hybrid System Design on Berkeley UAV 1 Introduction

Hierarchical Hybrid System Design on Berkeley UAV  T. J. Koo, D. H. Shim, O. Shakernia B. Sinopoli, F. Homann, S. Sastry Robotics and Intelligent Machines Laboratory University of California at Berkeley Berkeley, CA94720 Abstract

This paper describes recent results on system design and implementation of Berkeley UAV. The system design deploys the architecture of a Flight Vehicle Management System, FVMS, which combines planning and control. The resulting hierarchical control strategy which involves the interaction of continuous dynamics and discrete events is a hybrid system. Three controller based on dierent control methodologies are designed for various types of man uvres and ight modes, and their performance are evaluated under simulation based on a nonlinear model. The FVMS interacts with a vision system which is responsible for detection and recognition of dierent types of hazardous waste barrels. The vision algorithm consists of three parts: ltering, segmentation, and recognition. A 3D virtual environment simulation , SmartAerobots, is developed as a visualization tool. A helicopter-based aerial vehicle has been constructed and the proposed algorithms are being implemented and veri ed.

1 Introduction Unmanned aerial vehicles, UAV, have been found indispensable for various applications where human intervention is considered dicult or dangerous. Aerial vehicles based on rotary wings, such as helicopter[1, 2], have drawn signi cant attention. Despite its poor cursing performance, helicopter can be operated in dierent ight modes, such as vertical take-o/landing, hovering, longitudinal/lateral ight, coordinated turn. Due to its versatile in maneuverability, helicopter is capable to manuvre in and out of restricted areas and to hover eciently for long periods of time. It makes helicopter invaluable for power line inspection, terrain surveying, and investigation and clean-up of hazardous waste sites.

Figure 1: UC Berkeley UAV - Ursa Minor UC Berkeley UAV development team, BEAR, has built an autonomous helicopter planning and control system based on the architecture of a Flight Vehicle Management System, FVMS. The resulting hierarchical control strategy which involves the interaction of continuous dynamics and discrete events is a hybrid system [3]. FVMS is responsible for resolving con icts between air vehicle, planning of ight path, generating trajectory and sequence of ight modes, and regulating the ight vehicle along the trajectory. The FVMS interacts with a vision system which is responsible for detection and recognition  Submitted to

the International Aerial Robotics Competition, August 1998.

1

Ground system Remote system Figure 2: Hardware system con guration of dierent types of hazardous waste barrels. The vision algorithm consists of three parts: ltering, segmentation, and recognition. A 3D virtual environment simulation , SmartAerobots, is developed as a visualization tool. In the following section, we rst describe the hardware and software system. Then, system architecture of FVMS and the interaction with vision system is introduced in Section 3. Three dierent control designs for autopilot are described in Section 4. The sensor integration algorithm for the INS is explained in Section 5. In Section 6, the vision algorithm for barrel detection and recognition is introduced. The development of SmartAerobots, the visualization tool, is described in Section 7. Finally, we conclude our work and discuss issues for future research.

2 Hardware/Software System Design The system is designed to perform various aerial missions with minimal intervention from the ground station monitored by human operators. The overall system comprises of the aerial vehicle system and the ground monitoring station. The aerial vehicle agent is able to operate with the independent computing source onboard and power plant. The vehicle system receives the command of the ground station by radio communication link and reports the operation status and possible damages to the ground station. The aerial vehicle system can be categorized into three parts: the aerial vehicle platform, the navigation computer unit with sensors and the computer vision processor unit. To carry out versatile missions, helicopter-based VTOL is chosen for the platform. The aerial vehicle is based on the radio-controlled helicopter available in the market. Main and tail rotor blades are replaced with heavy-duty carbon ber reinforced ones to accommodate extra payloads such as onboard navigation system and sensors. The tail boom is also replaced with carbon ber boom to reduce the weight further and to eliminate the vibration of the tail rotor disc. The modi ed system allows the payload of more than 5 kg, which is sucient to carry the entire avionics system and vision processing unit. The overall system weighs 10kg, which includes the helicopter platform, onboard ight computer, radio communications, and the avionics sensors. The avionics installed onboard includes the ight management computer, inertial sensor unit, global position system (GPS), and additional sensors such as ultrasonic height sensor and digital compass. The ight computer system is constructed by so-called PC104 components so that the overall system is kept as compact and light-weight as possible. As the heart of the computing source, AMD 586 133MHz CPU with 8 MB RAM and 40MB Flash RAM is adopted. The CPU module is interfaced with A/D conversion board, serial communication board, PWM signal generator board, Flash RAM board and DC/DC converter board. The A/D converter reads the sensor outputs of the inertial navigation sensor unit, ultrasonic sensor and the digital compass. The PWM signal board generates the control signals for the ve servo actuators for engine throttle, main rotor collective/cyclic pitch and tail rotor collective pitch of the helicopter. The primary task of the ight computer is the IMU/GPS integrated navigation and the control of the helicopter. The 2

developed algorithms, which are introduced in the following, are implemented on the PC104 CPU module running on the real time operating system QNX. This OS provides a convenient way to schedule the timing of several modules running at their own rates. It also provides ecient method for inter-process communication to exchange the vehicle motion estimates and status of the vehicle. The most crucial part of the navigation system is the inertial navigation system, INS, consisting of IMU and the GPS. The IMU includes high precision three accelerometers and three solid-state rate gyros. To avoid introducing unwanted noise from vibration, the IMU is mounted at the nose of the helicopter using rigid metal platform. The outputs of these sensors are read by the A/D converter and then processed by the CPU module. To improve the accuracy of the estimates of the motion further, the navigation system is augmented with NovAtel Dierential GPS, DGPS. One module is installed onboard and the another GPS module is operated at the xed position where the accurate position is known. Separate radio communication link is established between these GPS modules to enable the DGPS operation, which provides the excellent accuracy of 2cm. This additional information is fed into the navigation unit via serial communication port to obtain the best estimate of the helicopter motion for stabilization and control. The conventional radio control system is also installed to the helicopter to provide a way to control the helicopter by human pilot in case of emergency. The control signals from the ight computer and the radio controller are switched by relays, which is controlled by a dedicated channel of the radio controller. To provide the capability of locate and recognize the barrels on the ground, the aerial vehicle is also equipped with the vision system. The vision system consists of the color camera and the dedicated vision processing unit. Separate CPU module is used for the vision information processing to lessen the computing load of the ight computer. The CPU board is interfaced with the color frame grabber. The information extracted from the incoming images is transferred to the main

ight computer via parallel port. Extremely small video transmitter is mounted on the helicopter to transmit the camera image to the ground station. The ground station includes the notebook computer, radio modem, video signal receiver and portable power source. The role of the ground station is to issue the navigation commands to the aerial vehicle system and monitors its status. Radio communication link is established throughout the mission and the status is displayed on the screen of portable computer. Video signal receiver and portable LCD display with recording capability are used to monitor the images being taken by the onboard camera.

3 System Architecture

The system architecture deploys the architecture of a ight vehicle management system(FVMS) that combines planning with control. The UAV executes tasks such as inspection of objects or surveillance of an area. FVMS was rst proposed for smart aircraft in the future Air Trac Management Systems(ATMS)[4, 5] for decentralized air trac control. FVMS is responsible for resolving con icts between air vehicles, planning of the ight path, generating a feasible trajectory and a proper sequence of ight modes, and regulating the helicopter along the trajectory. The operation of the proposed hierarchical control strategy involves the interaction of continuous dynamics and discrete events. Reasoning and decision making about the helicopter operation require a discrete representation, while the underlying dynamics of the helicopter and lower level controllers are described by dierential equations and continuous control laws. Since ight control systems have comparable functional and hierarchical complexity, systematic design tools are necessary in order to verify that the control schemes meet the performance speci cations. The reactive system consists of two parts: The Flight Vehicle Management System (FVMS) which is responsible for planning and controlling the operation of helicopter and the vision system which detects objects on the ground and identi es landmarks used for navigation.

3.1 Flight Vehicle Management System

The FVMS has a hierarchical architecture as shown in Figure 3 which consists of four layers, the strategic, tactical, and trajectory planners, and the regulation layer. Each layer of this architecture is described in details in [6]. We begin with a discussion of the strategic planner. 3.1.1

Strategic Planner

The Strategic planner resides on top of the FVMS and is concerned with the planning and execution of the central helicopter mission. The strategic planner commands the tactical planner to execute a sequence of proper behaviors which enable the helicopter to accomplish the overall mission. It designs a coarse, self-optimal trajectory which is stored in form of a sequence of four dimensional control points, ck . The strategic layer communicates with other FVMS in order to coordinate missions that require the cooperation of multiple helicopters. 3

Strategic Planner Discrete Event System control points Tactical Planner yd

detect

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Figure 3: Proposed FVMS Architecture for helicopter 3.1.2

Tactical Planner

The tactical planner is responsible for the coordination and execution of behaviors, such as landing, searching an area, steering towards a way-point, collision avoidance or inspection of objects on the ground. The tactical planner is able to overrule the behavior proposed by the strategic planner, in case of safety critical situations such as collision avoidance, failure of sensors or strong wind gusts. The tactical planner returns to the original behavior as soon as the con ict among the prioritized safety manuvre and accomplishing the mission is resolved. The strategic planners of all helicopter involved in the potential con ict determine a sequence of manuvers which will result in con ict-free trajectories, either by communicating with each other through wireless data-link, or by calculating safe trajectories assuming the worst possible actions of the other helicopter. In addition, the tactical planner re nes the strategic plan by interpolating the control points with a smooth output trajectory, denoted by yd in Figure 3. The tactical planner uses a kinematic model of the helicopter for all trajectory calculations. The output trajectories of the kinematic model are then passed to the Trajectory Planner as desired output pro les. 3.1.3

Tra jectory Planner

The trajectory planner decomposes the behavior commanded by the tactical planner into a sequence of primitive manuvres, such as forward-, sideward- ight, hovering, vertical climb and turns. The trajectory planner can employ a variety of basic

ight mode controllers which are designed to control dierent variables in the helicopter dynamics. Each controller emphasizes dierent performance attributes, such as tracking accuracy, robustness and execution speed and therefore oers advantages for a speci c type of manuvres. The trajectory planner activates those basic ight controllers that are best suited to achieve the proposed behavior considering the context of operation and the current ight mode. In addition, it computes a feasible nominal input trajectory for the continuous regulation layer. The trajectory planner has to guarantee a safe, smooth transition among ight mode controllers, controlled dynamic variables and nominal input trajectories. These issues constitute the hybrid control problem that we address in more detail in [6]. The trajectory planner uses a detailed dynamic model of the helicopter, sensory input about the wind's magnitude and direction, and the tactical plan consisting of an output trajectory, to design a full state and nominal input trajectory for the helicopter, and the sequence of ight modes necessary to execute the dynamic plan. These ight modes represent dierent modes of operation of the helicopter and they correspond to controlling dierent variables in the helicopter dynamics. The resulting trajectory, denoted yd , xd , and ud in Figure 3, is given to the regulation layer which directly controls the helicopter. 3.1.4

Regulation Layer

Once a feasible dynamic trajectory has been determined, the regulation layer is asked to track it by using the ight mode controllers assigned by the trajectory planner. In the presence of large external disturbances (such as wind shear or malfunctions), however, tracking can severely deteriorate. The regulation layer has access to sensory information about the actual state of the helicopter dynamics, and can calculate tracking errors. These errors are passed back to the trajectory planner, to facilitate replanning it of the trajectory or even switching the controller if necessary. 4

uM uT uB uA

θM Actuator Dynamics

θT B A

TM Rotary Wing Dynamics

TT a 1s b1s

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fb τb

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Figure 4: Helicopter dynamics

3.2 Vision System

The helicopter perceives its environment by means of a video camera. A vision system analyzes the image data in order to inspect objects on the ground, detect landmarks for navigation and other helicopters operating in the same area. During the mission, the camera consistently scans over the sensible area and sends a signal to the tactical planner if it detects an object. In reaction to the detected object the tactical planner switches to an investigation behavior, which requires to recompute the trajectory and to change the ight mode. The vision system continuously estimates the position of the object relative to the helicopter and passes this information to the tactical planner for regenerating the trajectory.

4 Control Design A complete model of a helicopter can be divided into four dierent subsystems, which are actuator dynamics, rotary wing dynamics, force and moment generation processes, and rigid body dynamics. The connections between subsystems, and state and control variables are de ned in Figure 4. A number of researches [7, 1, 8] have been published about the development of nonlinear models for the aerodynamic properties of the main rotor and the tail rotor in hover or in forward ight. In this research, the results of these literatures are gathered to generate the appropriate model valid in the hover and low velocity region. The equation of motion may be obtained by nding the functions describing the forces and moments exerting on each major components of the helicopter, namely, main rotor, tail rotor, fuselage, horizontal and vertical stabilizers. The equation of motion is obtained by equating the sum of force and moment terms in each direction to the time derivatives of linear or angular momentum. For the helicopter autopilot at the regulation layer, we employed three dierent control methodologies to design controllers responsible for dierent types of manuvres and ight modes. A comparison of the performance of the controllers are shown in [9]. For set-point regulation and mild trajectory tracking, we apply robust control using {synthesis[10]. The control design emphasizes on a robust performance in regard to model uncertainties, sensor noise, parameter changes and external disturbances. Evolutionary algorithms provide an eective means of tuning fuzzy logic controllers. In case of multiple con icting design goals, the genetic fuzzy system[11] generates a Pareto optimal set of feasible solutions to the control problem. Depending on the performance speci cations the designer selects the fuzzy controller that is most appropriate for a speci c type of manuvre. Finally, feedback linearization[12] deploys a nonlinear dynamic model of the helicopter for the control design. The nonlinear controller is capable of tracking aggressive ight trajectories, since the design is applicable over the full operational

ight envelope.

4.1 Linear Robust Multivariable Control Design

During the last decade, a number of researches have been performed on the design of the robust linear controllers for the helicopter control in hover or steady forward ight[13, 14, 15]. Due to the inherent cross-coupling of the rotor dynamics, MIMO control algorithms have been preferred instead of using SISO controllers for roll/pitch/yaw axis. Also, the controller must perform stabilization of the nonlinear unstable helicopter system in the presence of uncertain and/or poorly known system dynamics and the severe disturbance and sensor noise. To design a controller satisfy these conditions, the -synthesis control theory is applied to the hovering controller for the helicopter in this research. Especially, the -synthesis theory provides a explicit and systematic way to model the parametric uncertainties in the system and yields the controller satisfying the robust stability and performance speci cations. The uncertainty or unmodeled dynamics of the helicopter system equation may be categorized as: 1) poorly identi ed or time-varying aerodynamics or inertial quantities, 2) unmodeled higher order dynamics such as rotor apping dynamics or servo actuator dynamics, 3) nonlinear eects of governing equations. All of these may perturb the resulting closed-loop linear control systems out of stable region, so the controller should be designed to robust to those eects. Among those, special attention has been given to the variation of the rotor speed, which has signi cant impact on the variation of the system dynamics. The resulting control design is shown in Figure 5 5

Figure 5: The interconnection diagram of the control design

4.2 Fuzzy Logic Control Design with Evolutionary Tuning

Among many other applications, fuzzy logic control has been applied to control an intelligent unmanned helicopter [16]. The helicopter autopilot is composed of four separate modules which correspond to the control actuators collective pitch, tail rotor pitch, longitudinal and lateral cyclic pitch. The complexity of the control design can be reduced by carefully considering the helicopter dynamics when subdividing the system into separate modules. . .

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Longitudinal and lateral cyclic pitch Figure 6: Fuzzy controller architecture

The collective pitch control block attempts to follow a commanded altitude in vertical climb and descent. The heading control block governs the yaw motion during turns and compensates the anti-torque generated by the main rotor in order to maintain a desired heading. The longitudinal and lateral block regulate the horizontal motion and at the same time control the attitude to maintain the helicopter stable. Individual modules contain multiple fuzzy controllers organized in a hierarchical manner that re ects the eect and coupling of dierent controls. Each block employs a switch which selects the controller that is specialized for the current ight mode. During a transition between position and velocity control the switch smoothly interpolates among the proposed control actions. Evolutionary algorithms constitute a class of search and optimization methods, which imitate the principles of natural evolution. Phillips et al. proposed a genetic algorithm algorithm to learn fuzzy logic controllers for helicopter ight [17]. Our approach employs an evolution strategy that operates on vectors of real numbers which correspond to the gain factors 6

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Figure 7: The approximate, dynamically extended helicopter model in the conclusion part of fuzzy rules. The population is initialized with the parameters of the hand designed fuzzy rules. An incremental learning scheme gradually expands the genome and thereby re nes the fuzzy knowledge base that acquires additional fuzzy rules. The coding scheme, the genetic operators and the structural expansion procedure is described in detail in [18]. The performance of a fuzzy controller is evaluated in a simulation of the helicopter. The designer speci es his design goals by means of a scalar objective function, which is formed by a weighted sum of cumulated state errors.

4.3 Nonlinear Tracking Control Design

Input-output linearization[12] has been applied in a number of practical applications in solving output tracking problems of nonlinear dynamical systems including VTOL, STOL and ghter aircraft [19, 20, 21]. The design methodology makes direct use of the inherent nonlinearities of the plant model and transforms the input-output map of the original nonlinear system into a decoupled linear time-invariant form. Thus, the transformed system can be controlled by applying well-developed linear control design technique. On the other hand, the design of a single nonlinear controller remains applicable over the full operational ight envelope. Hence, the control design is feasible for tracking aggressive (fast) trajectories. However, there is a large class of physical systems which do not satisfy the restrictive conditions for input-output linearization. Instead, approximate input-output linearization [19] is applied. The basic idea of approximate linearization is to neglect the small parasitic couplings between the mechanisms of force and moment generation, then apply input-output linearization. In [22], it has been shown that, the approximated system with dynamic decoupling is linearizable without zero dynamics. A block diagram showing the structure of the approximate, extended system is presented in Figure 7. The extended system is dierentially at since the system can be linearized by feedback. Thus, one can generate approximate state and nominal input trajectory for the true system from the output trajectory for state tracking purpose.

5 Inertial Navigation System The Inertial Navigation System, INS, supplies navigation information such as vehicle position, velocity, 3 axes of attitude (roll, pitch, yaw), 3 axes of angular rate and linear acceleration, and system quaternion. An Extended Kalman Filter[23], EKF, has been designed for combining the sensor information from the DGPS, IMU and compass to provide navigation purpose. The integration of DGPS and IMU are done in a loosely coupled way; two systems are running independently and no IMU data are used by DGPS. DGPS provides position of a vehicle in various coordinate frame formats, such as Earth-Centered Earth-Fixed, ECEF, frame. For sensor integration purpose, DGPS data are chosen to be represented in Wander-Azimuth frame[24], also known as computational frame, format. The frame is de ned with respect to the earth frame by three successive Euler angles (longitude, latitude and wander angle). DGPS data provides information about longitude, latitude and altitude above the reference ellipsoid measured along the normal passing through the point of interest. Compass is used for providing heading information of the vehicle. Since the magnetic north and the true north are oset by wander angle, the compass data are compensated by the dierence. IMU provides 3 axes of angular rate and acceleration with respect to the body frame in which the x axis pointing vehicle's longitudinal axis, the y axis out to the right side, and the z axis pointing down. The navigation frame is chosen to be the Wander-Azimuth frame. For the rigid body motion, we can derive the equations of motion using Newton-Euler equations. Since IMU only provides angular rate but no angular acceleration, the following matrix equations are used for integration.

p_ = v v_ = Rab + bI g R_ = R!^ b 7

(1) (2) (3)

where p; v 2