Collision Avoidance Technique Using Bio-mimic ...

Collision Avoidance Technique Using Bio-mimic Feedback Control Quazi Delwar Hossain1, Mohammad Naim Uddin1*,

Md. Mahmudul Hasan1**

1 Department of Electrical and Electronic Engineering Chittagong University of Engineering & Technology,Chittagong-4349, Bangladesh [email protected]*, [email protected]**

Abstract- Collision avoidance is a major area of research in vehicle control & safety that involve varying degrees of uncertainty. In general, this problem is challenged with forward collision avoidance technology (FCAT) in cars. An intelligent technique that complements the driving experience must work to avoid collisions generating a smooth trajectory, the vehicle to the intended destination as quickly as possible. Unfortunately, satisfying these requirements with traditional methods proves intractable and forces us to consider bio mimic techniques like Swarm Intelligence. We have developed a biotracking based hybrid model which follow the behavior of Ant (tapinoma melanocephalum) and Bat (molossus molossus). In this research, our robot detects obstacle with ultrasound like bat and makes decision to avoid the obstacle like ant. Here an Ultrasonic sound (SONAR) reflection based intelligent control strategy is implemented. Depending on Bat object tracking and Ant motion strategy, we have successfully avoided collision between prototype vehicles and obstacles which was our first goal. This natural sense building up in mechatronics world could make life more secure and find out a perfect relation between theoretical mathematics, nature and engineering. Keywords—collision avoidance; bio-mimic; intelligent robotics; echolocation; smart traffic; hybrid sensor;

I. INTRODUCTION Every day around the world, almost sixteen thousands people die from injuries of various collisions. For every person dies, several hundreds more are injured, many of them with permanent sequels of injuries. An estimated 1.24 million people lose their lives in road traffic crashes every year, and another 20 to 50 million are injured [1]. This problem of road, air, even water traffic crashes and resulting injuries and fatalities is however more acute in all over the world. Even the so called renowned technologies like FCAT (forward collision avoidance technology), ACC (adaptive cruise control) [2] etc. are found not so effective both economically & technologically also. There are some other common security systems like TCAS (Traffic collision avoidance system) or any other Air-Borne collision avoidance system in which the vehicle is guided from a central station [3]. But, those are not really practical to apply in cars or anything except planes. We have seen the Python driverless car project and some other projects like this but they still require lots of modifications. Furthermore, they do not implement any bio mimic technique so far which are naturally precise and fault less.

Our nature is adorned with plenty of resources. Thousands of perfect algorithms give security to them. In case of collision avoidance, biological movements of thousands of animals inspired us very much. Flock of fishes, thousands of birds, bees, ants, bats, etc. is living in organization. The organized or colonial livings have their own unique pattern. Their harvesting and food chain also maintain some pattern. Moreover their random movements also have their unique strategy. Investigating their mysterious motions inspire us. We observed many living beings movements, their internal communication and hunting food. Dolphin (delphinus delphis), Bat (molossus molossus), Platypus (Ornithorhynchus anatinus) use ultra sound for their communication and obstacle detection [4]. Receiving the feedback signal these animals take decision which way to move and how to avoid the collision [5]. They learn or train themselves by the Social Intelligence (SI) system. Exchanging their previous memory and information, they gather their colony in a specific location. Honey bee (apis dorsata) and Ant (tapinoma melanocephalum) are the two perfect examples of the event. Ant perfectly maintains the collision avoidance between its body and obstacles. They only touch with their antenna and communicate with each other. The ultra sound based obstacle detection technique and this colonial collision avoidance technique inspire us to design and implement a hybrid system for collision avoidance. In this work we present an idea that can be implemented in traffic safety by the application of Robotics & Computer Vision through bio-mimic intelligence. This trend of multiple vehicle accident, major collision types, casualty type, from which we want to get rid of or at least minimize the total number. II. OBJECTIVES OF THE WORK The aim of this research is developing a feedback controlled collision avoidance technique and implement with a prototype robot. The objectives of this work are:  Studying the obstacle detection technique using ultra sound like Bat (molossus molossus)  Studying the colonial movement and collision avoidance technique of Ant  Questing for new unapplied technique to apply in collision avoiding.

 Bridging biological sensing intelligence mechatronics system to avoid collision

with

 Developing a methodology to equip any moving system to make fully collision free. Hence, a new hybrid model of a collision avoidance algorithm will be developed and used in our day to day life. III.

ECHOLOCATION IN ANIMAL

There are several groups of animals which detect any obstacle or any prey using the interpretation of transmitted ultrasound by them and the reflected pattern of ultrasound from the environment. Birds, cetaceans and bats are echolocate by transmitting simple or complex series of clicks. The users of this technique i.e. Echo locators use ‘‘the difference between what they say and what they hear’’ to collect information about their surroundings. In the suborder microchiroptera, however, echolocation has become much more sophisticated. The 813 species of small nocturnal bats all echolocate, making use of structured tonal signals rather than simple broadband clicks. They have developed many varied cholocation adaptations; have brains that are adapted for processing acoustic signals, and exhibit a wide variety of ear and nose sizes and shapes to improve the focusing of transmitted and received sound waves [6]. A. Bat Echolocation strategies Bats use ultrasonic sound for navigation. Their ability to catch flying with insects while flying with full speed in pitch darkness is astounding. Their sophisticated echolocation permits them to distinguish between a moth (supper) and a falling leaf. About 800 species of bats are grouped into 17 families. The ultrasonic signals utilized by these bats fall into three main categories. There are two suborders, Megachiroptera and Microchiroptera. Megas use short clicks, Micros use the other two. Tongue clicks produce click pairs separated by about 30 ms, with 140-430 ms between pairs. (Sales and Pye, Ultrasonic Communication by Animals). 1060 kHz in frequency swept clicks. One kind of bat, the verspertilionidae, has frequency swept pulses 78 kHz to 39 kHz in 2.3 ms. It emits pulses 8 to 15 times a second, but can increase to 150-200/s when there is a tricky maneuver to be made. Bats transmit two types of acoustic signals: either constant frequency (CF) or frequency modulated (FM). Some bats transmit CF only, some FM only, and some can both. There are three distinct phases to the echolocation attack sequence of a bat approaching airborne prey: detection, approach, and terminal. During the detection phase, the bat transmits long CF pulses, at low pulse repetition frequency. The approach pulses are shorter CF and are transmitted at higher pulse repetition frequency. Terminal phase pulses are descending FM of very short duration with high bandwidth and high pulse repetition frequency [7]. B. Ant collision avoidance At the time of movement, for hunting food, an ant follows special moving sequence to track the destination path where initially they are scattered but finally they find an optimised path leading to the food [8] and always tries to meet with other

agents. At the most critical situation, ants use their social intelligence system and try to come back to the right path or destination [9]. To describe in brief, the flow chart (see Fig. 1) shows that when an ant starts to move for a particular destination; it continuously keeps detecting for obstacle edge and it shares the knowledge of the path to others and thus making a social intelligence (SI) network. Finally it reaches to the goal. Initially the path is very scattered but with time, the final path of the colony is an optimized path. Start

Initialization

Yes

No

Termination

Obstacle

No

Edge Detection Add agent Yes New agent Learning SI

System End Fig. 1 Flow chart of path termination.

IV. SENSOR SELECTION Since the goal is to design and make a feedback controlled collision avoidance technique, at first we quest the technique how a feedback will be received from any obstacle. If we perfectly identify the accurate object, shape and distance, it will be easier to avoid the collision with that object. Several studies have reported We identify various methods of feedback signal from object like light intensity, temperature of the object, infrared emission, echo of ultrasonic sound, electromagnetic wave, etc. But these kinds of wave are not so easy to receive without distortion due to the following reason.  Light sensor is one of the preferable choices. But different light intensity and day night problem is one of the major facts. It is useable for light goal based movement of system.  Ultrasonic sound reflection, SONAR is a good option for economic use and for day-night condition.

Considering these facilities SONAR sensor is the best option for us. It is cost effective, light, considerable range. So we decided the ultra sound bases object tracking technique. More over the analog output of sound sensor make possible to consider about the fuzzy logic. A. Fixing the tracking procedure We observed the object tracking algorithm of Bat (molossus molossus). Bat maintains the three dimensional objects tracking with the sound reflection and distance measuring technique. By applying same technique we measured the distance of object or sound reflective source on the path of moving path of robot. Initially our work is only for two dimensional plane. B.

Software interfacing and Mathematical explanation

We analyzed the path of ants and robot individually. This observation illustrated that way of thinking of ant is so diverse and it indicates some mysterious social behaviors and swarm intelligence of ants. However, we tracked both the trajectory path of ant and robot. The co-ordinates of the paths were generated by first capturing their video clips and then by using OpenCv. By using the generated sample values of the coordinate we established a mathematical model of the two dimensional line locus functions of X & Y in MATLAB. We use the curve fitting tools to fit the curve and modeling this with equations. There are lots of curve fitting techniques availabled in MATLAB.

actuator direction () motor drive (front) return -1 end if for (i=0 to n) position check (back 180) sensor value (average) end for if (adequate distance back) actuator direction () motor drive (back) return -1 end if if (! adequate distance) loop () end while (infinite loop) end procedure Fig.2 describes a flow chart of the intelligent collision avoidance technique of robot. Start

Initialization Forward path

C. Hardware development and prototype for study After successful study about the bats and ants we have developed special algorithm for our robot to avoid the collision, and implemented it at our two different robots. Using two different SONAR transceivers each of which covers 180° each we have covered total 360° polar coordinate scanning. We detect the position of object and identify the edge. For edge detection technique, Kalman filter is more popular method for noise illumination. But at this stage the developed system does not require that kind of high level filtering for edge detection.

Obstacle edge detection by SONAR

Adequate free path Backward (180ᵒ)

No

Yes

The pseudo code of the advance intelligent robot algorithm is given below: Yes

Collision avoidance intelligent robot algorithm Rule base algorithm *************************** procedure Exact Program () bot setup () while (infinite loop) sonar initialize () for (i=0 to n) position check (forward 180) sensor value (average) end for if (adequate distance front)

Adequate free path Forward (180ᵒ)

Actuator direction set

No

Move

End

Fig 2: Collision avoiding Intelligent Robot flow chart

V. EXPERIMENTAL ANALYSIS A. Ant event sampling Ant shows different scattered path and moves in a random manner and hereby we have seen plenty of varieties of ant paths. Hence, we have sampled them and three different events we have taken under consideration as per our necessity while rest is considered as random events.

Where, p1, p2, p3, p4, p5, p6 are different constant. The general model of exponential equation is

f ( x )  a exp (b  x)  c exp ( d  x ) Where a, b, c, d are different constants. For curve fitting we considered Goodness of fit, SSE (sum of square error), Rsquare, Adjusted R-square & RMSE.

Case “A”: Ant goes up corner from the static obstacle object. Case “B”: Ant goes down corner from the static obstacle object Case “C”: Ant just overpasses the static obstacle and forward as previous. B.

Robot and ant event sampling

After getting the system information and considering the events (which have three different cases); a mathematical model was established. On the basis of this mathematical model our bot was designed. At real field environment our robot performs these kinds of characteristics when faces an obstacle.

Fig. 3: Random sampling of events for Ant over 50 different samples (only three cases are concerned otherwise considered as random events).

Robot follows case A (going up corner after detecting obstacle) & case B (going down corner after detecting obstacle) well. Over 50 random sampling considering this three cases bot gives the statistic. TABLE I PERCENTAGE OF DIFFERENT CASES FOR ANT & ROBOT Case:

Ant (%)

Robot (%)

Up pass (A)

20

36

Down pass (B)

30

50

Random

35

14

In pie charts (See Fig. 3, 4) three different cases are shown by four different colors. Red color indicates up corner event, blue for down corner, and green for over pass and finally the violet color indicate the random events. We get that the tendency of showing random behavior in ant is far more than our robot. This is due to the fact that the robot was not programmed to show random characteristic, rather it was designed to avoid the obstacle in a particular manner. VI. MATHEMATICAL ANALYSIS With the help of computer vision technique of image processing (open CV, software) we follow the line path of ant and robot. After that, receiving the coordinates using “processing” software we get the path locus. Using MATLAB curve fitting tool we fit equation for the curve locus. For curve fitting we consider standard exponential and five degree polynomial expression.

Fig. 4: Random sampling of events for Robot over 50 different samples (only three cases are concerned otherwise considered as random event).

Case: A Black line indicates motion path. Red circle indicates obstacle. Both ant and bot goes up corner from the obstacle object after meeting with obstacle. In Fig.5, left side is Ant’s path; right side is Bot path trajectories which are drawn by Processing soft.

The general linear model of five degree of Polynomial equation: f x   p1  x 5  p 2  x 4  p3  x 3  p 4  x 2  p5 x  p 6

(a)

(b)

Fig. 5. Comparative study of obstacle passing through up corner (a) for ant (b) for robot [Processing soft] *

Defused path exponential expression for ant (left) and robot (right) is shown in Fig.10.

Fig. 6 Polynomial curve for case A (left ant & right bot)

Fig. 10. Case B exponential curve (left ant & right bot)

Here our robot could not follow the tendency of ant because the ant just overpassed the obstacle but the robot was not designed to do so. Based on karl peareson’s coefficient of correlation we can make decision about the accuracy of this work. The unity factor indicates the successfulness between two different curves. Fig. 7 Exponential curve for case A (left ant & right bot)

The four different (a, b, c, d) constants indicates the fitness of curve for polynomial and exponential equation shown in Fig.6 and Fig.7 respectively. The dotted line is the real trajectory path of bot or ant while the red smooth line is regarding polynomial or exponential curve achieved using curve fitting tool in MATLAB. This procedure is also the same for Case: B.

The five degree polynomial distribution curve correlation coefficients are given below for case A & B.

Case: B Both ant and bot goes down corner from the obstacle object after detecting with obstacle. Ant comes so close at the object and also touch with the object with its antenna. On the other hand the bot doesn’t come as close as ant. Because the distance measuring ultra sound sensor using Fuzzy distribution the robot makes decision to change its direction with decision making unit. Fig. 11. Comparison among correlation coefficients of Ant & Bot path trajectory (for polynomial expression).

Correlation coefficient, (a)

r

 Y Y   Y  Y  1

2 1

2

2 2

(b)

Fig. 8. Comparative study of obstacle pass through down corner (a) for ant (b) for robot [Processing soft] *

Getting the co-ordinates of ant path trajectory and Bot path trajectory from image processing using ‘OpenCv’,we compared between the co-ordinates taking the ant path as reference by using the above equation where ‘r’ is coefficient of correlation and Y1,Y2 are respective deviations from mean value. From Fig.11 it is found that for the case A and B both possesses positive coefficient of correlation between ant and Bot path trajectory.

Fig. 9. Case B polynomial curve (left ant & right bot)

TABLE II COMPARISON BETWEEN REAL ANT & BOT Case

Correlation coefficient of polynomial curve (r)

Up pass (A)

0.975352

Down pass (B)

0.98650

VII.

CONCLUSION & FUTURE WORK

In this work, the relation between theoretical mathematics, nature and perfect engineering have been investigated. We have followed ant and ant colony while studied their response facing any obstacle and also made our effort to implement it in robotics together with the combination of Bat’s ultrasound echolocation technique. A comparative mathematical study between the complex modes of behavior of ant and robot is also accomplished. Next, our motto is to set a goal for the robot just like a single ant moves following the smell of food and considerable natural creatures like honey bee, birds, butterflies, fishes so that the robot can reach its destination without any collision applying artificial potential field along with fuzzy-fication and so on which will lead us to a perfect driverless vehicle. ACKNOWLEDGMENT We state our gratitude to the Department of EEE & Math for extending their generous help, encouragement. We are thankful to the RMA (Robo mechatronics Association, CUET, Bangladesh) for helping us in our research work. Special gratitude to the students who are supported us to collect ANT and take video shots of ants which were tracked. REFERENCES [1] [2]

[3]

[4] [5]

[6] [7] [8]

[9]

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