International Journal of Computer Engineering and Applications, Volume XII, Special Issue, May 18, ISSN

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1 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, May 18, ISSN UAV AND UGV STATE-BASED BEHAVIORAL FORMATION CONTROL FOR LANDMINE OPERATIONS Moustafa M. Kurdi Belarusian National Technical University BNTU Minsk, Belarus Abstract Robot formation control has drawn important consideration for a long time, and now it is surely known and developed in its field. Initially, this paper is dealt with planning the movement of air/ground robots in Behavior Formation Control, which means certain behavioral constraints limitations are forced on the relative positions and orientations of the robots throughout their travel. Second, it describes the projection of landmine detection on GIS-map based-on the ground penetrating radar (GPR) attached to unmanned aerial vehicle (UAV). Third, it identifies how the unmanned ground vehicle (UGV) uses GIS-map to analyze path in real-time and to remove landmines. Fourth, it focuses on the cooperation between air and ground robots by using an efficient unwired transmission through base station. Behavior-State Formation System uses a control strategy of Finite State Automata for landmines detection, GISmap uploading/downloading, and landmines removal. This proposed system will demonstrate how the class of Imad Elzein Belarusian National Technical University BNTU Minsk, Belarus imad.zein@liu.edu.lb behavior formations of hybrid robots (air-ground vehicle) can be used effectively for landmine operations. Keywords unmanned aircraft vehicle (UAV); unmanned ground vehicle (UGV); Behavior Formation Control; hybrid robots; landmine operations I. INTRODUCTION Robot formation control [1 ] has drawn significant attention for many years especially for ground mobile robot and quadcopter. The ability to keep up a specific formation of hybrid robots is of particular where information should be gathered from various inputs or resources. A controller is actualized for every robot to guarantee it tracks its planned path. Robot formation control is done by setting up xyz system rather than xy system [2]. Moustafa M. Kurdi and Imad Elzein 1

2 UAV AND UGV STATE-BASED BEHAVIORAL FORMATION CONTROL FOR LANDMINE OPERATIONS Researchers classify formation control strategies [3] into three strategies: i) behavior-based; ii) virtualstructure; and iii) leader-follower. A particular approach of our hybrid robots using the Behavior- Based (B-B) formation starts by designing simple behaviors or motion primitives for each individual robot, e.g., formation keeping, trajectory tracking, goal seeking, landmine detection, and obstacle avoidance. The approach is potentially applicable in many other domains such as search and rescue, agricultural coverage tasks and security patrols. Behavior-based approaches are also useful in guiding a multi-robot system in an unknown or dynamically changing environment using vision processing system. This article discusses Behavior-Based (B-B) formation of UAV (Phantom-4 Pro Visio) and UGV (Belarus-132N) as follows (Fig.1): 1. Quadcopter takes off, moves over the region, photograph the area, searches and detects landmine by ground penetrating radar. 2. Quadcopter projects the landmine findings into Geographic Information System (GIS) Mapping. 3. Quadcopter transmits GIS images, collected data to base-station located near to the field of operation. 4. Central Unit Base Station uploads the GIS landmine findings into digital-map, and then it sends the updated digital map to the Ground Robot. 5. Ground Robot uses digital-map to move through the operational area and remove the landmines. This paper is organized as follows: Section II reviews the state-based behavioral formation control, and Section III presents UAV-UGV behavioral formation control with DFA for UAV and UGV control. Our conclusions and thoughts on future extensions are summarized in Section IV. II. STATE-BASED BEHAVIORAL FORMATION CONTROL State-based modeling is a modeling concept used to specify the complex hybrid control systems where the entire objective of the system is divided into set of functional task achieving behaviors/motion states working on individual goals concurrently and asynchronously, which upon integration yields the global objective of the system. Fig.1. General Architecture of the Proposed System The behaviors or motion states of the robots are modeled and expressed in state based model as: ρ = C (G*B(s)) (1) where, * is the vector encoding the global responses undertaken by the robot; G= [g1, g2, g3..] T is the vector encoding gain of each behavior β i, ; B is the Vector of all active behavior β i at time t, B= [β1, β2, β3 ] T ; S= [s1, s2, s3 ] T is the vector of all stimuli s i for each behavior at time t; and C is the Coordination/arbitration function which is competitive or cooperative. Autonomous robotic systems present several difficult challenges. An autonomous robotic system must be extremely self-reliant to operate in complex, partially known and challenging environments using its limited physical and computational resources. In spite of these difficulties, the control system must ensure in real time that the robot will achieve its tasks. A. Finite State Automata Approach A set of behaviors that is adequately competent to handle the situation corresponding to the given state is selected. Using this formalism, systems are modeled in terms of Finite State Automata (FSA), where states correspond to execution of actions/behaviors and events, corresponding to observations and actions, cause transitions between the states. FSA consists of two types: i) Deterministic Finite Automata (DFA) There is a fixed number of states and we can only be in one state at a time; and ii) Nondeterministic Finite Automata (NFA) There is a fixed number of states but we can be in multiple states at one time. The DFA is represented as: A = (Q,, δ, q o, F). III. UAV-UGV BEHAVIORAL FORMATION CONTROL The system describes a formation of 2-robots [8], ground robot Belarus-132N and ground penetrating radar GPR attached to quadcopter Phantom-4, as shown in Fig.1. The Moustafa M. Kurdi and Imad Elzein 2

3 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, May 18, ISSN proposed control algorithm is applying State-Based Behavioral control formation [9]. A. Quadcopter Phantom-4 Pro Control The UAVs are usually used for military, and nonmilitary operations such as landmines detection. Phantom-4 Vision Quadcopter is a light-weight, multifunctional aircraft, and equipped by Ground Penetrating Radar (GPR). Fig 2: UAV Transition diagram of DFA for landmine detections a) UAV Deterministic Finite Automata A proposed system of UAV is depicted in Fig. 2, where the mission consists in finding landmines, locating their position on GIS-map and saving data into base station sitting near the operational region while avoiding obstacles. The agent and its interaction with the environment are modeled by a FSA and is denoted the plant. Figure 2 shows the transition diagram for DFA for UAV landmine detection. Node A represents Start ; Node B represents Find a landmine ; Node C represents Locate landmine object into GIS map. There is a Start arrow entering the start state, A, and the final state, B, is represented by a double circle. The transition state 0 represents cancel an action (landmine detection), while 1 represents do the action (landmine detection). The DFA formal definition for UAV landmine detection is: 1. Finite set of states Q={A, B, C} 2. Input symbols Σ={0, 1 } 3. Start state A 4. Final state F={B} 5. A transition function δ δ (A,0)=A // do not start δ (A,1)=B // go-to Find landmine state δ (B,0)=B // cannot find landmines δ (B,1)=C // New landmine was found, so locate it at GIS-map δ (C,0)=C // New landmine object cannot be added (located) to GIS-map due to technical or communication errors. δ (C,1)=B // search for another landmine b) UAV Landmine detection UAV projects each landmine found with a vertex on the GIS-map and transfer it to base station. Each landmine found has the following properties: 1) ID: Unique number assigned to the new landmine. 2) Position of new object (x r, y r). 3) Label the dimension of new object (l r, w r, d r). 4) Object type (Anti-personal, anti-tank). Fig 3: UGV Transition diagram of DFA for landmine removal Fig 4: UGV Model for Obstacle Avoidance B. Ground Robot Belarus-132N Control Ground Robot Belarus-132N consists of fourwheeled, serial chassis of Belarus-132N tractor with Moustafa M. Kurdi and Imad Elzein 3

4 UAV AND UGV STATE-BASED BEHAVIORAL FORMATION CONTROL FOR LANDMINE OPERATIONS 120cm length, 120cm width, 180cm height, and weighing about 500 kg. a) UGV Deterministic Finite Automata A proposed system of UGV is depicted in Fig. 3, where the mission consists in uploading GIS-map from base station, removing landmines, updating GIS-map and saving updated map into base station while avoiding obstacles. Figure 3 shows the transition diagram for DFA for UGV landmine removal. Node A represents Start ; Node B represents Upload GIS map to UGV ; Node C represents Remove landmine from the ground ; Node D represents Update GIS-map after one landmine removal. There is a Start arrow entering the start state, A, and the final state, C, is represented by a double circle. The transition state 0 represents cancel an action (remove landmine or upload GIS), while 1 represents do the action (remove landmine or upload GIS). The DFA formal definition for UGV landmine removal: 1. Finite set of states Q={A, B, C, D} 2. Input symbols Σ={0, 1 } 3. Start state A 4. Final state F={C} 5. A transition function δ δ (A,0)=A // do not start δ (A,1)=B // upload GIS-map from base station in order to start landmines removal δ (B,0)=B // cannot upload GIS-map δ (B,1)=C // GIS-map was uploaded, goto remove landmine state δ (C,0)=C // cannot remove (extract) landmine due to technical errors. δ (C,1)=D // update GIS-map after one landmine object removal δ (D,0)=D // cannot update GIS-map into base station due to technical and communication errors. δ (D,1)=C // GIS-map was updated, go-to remove landmine state to continue removing all landmines. Fig 5: UGV Model for Tracking IV. UGV REMOVE LANDMINE STATE The State C which represents Remove landmine from the ground, as shown in Fig. 3, consists of two sub-finite Automata (Navigation and Obstacle Avoidance; Tracking). A. Scanning and Analysis of the selected area The three stages for the field of view of the robot with obstacle avoidance strategies are: 1. Based on the distance and the closest object (obstacle) in the robot's path, we figure the suitable turning rate, which is proportional to the ratio of obstacle distance and to the velocity of the robot. 2. While the UGV robot is directing far from the obstacle, the system show the location of the obstacle in the field of view. 3. When the field of view is without obstacles, the UGV robot will start adjusting for the angle of deviation and reach the desired goal and path. B) Obstacle Avoidance Behavior Obstacle avoidance behavior system shows the field of view and free space in front of the mobile robot (Belarus132N). When the obstacle is labeled the avoidance strategy is applied. Obstacle avoidance behavior is modeled as (Fig. 4): 1. States Q = {S 1 ; S 2; S 3; S 4} where S 1 represents Rest, S 2 represents Moving, S 3 represents Steer Away, and S 4 represents Following the path. 2. Events Σ= {a; b; c; d; e; f; g; h} where a = motion detected; b = motionless; c = obstacle detected; d= move and avoid; e = stopped; f = path finished; g= path evaluated; h= clear and free the path. 3. Initial state q 0 = {stable} 4. State S 1 = rest: Mobile robot is stable (at rest). Moustafa M. Kurdi and Imad Elzein 4

5 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, May 18, ISSN State S 2= moving: Mobile robot is moving ahead and sensors are continuously monitoring the free space in front of the robot. 6. State S 3= steer away: Mobile robot is faring away from the obstacle with a particular velocity. 7. State S 4= following the path: The path is being followed using feed forward control strategy. 8. Event a= motion detected: is asserted when motion has been detected. 9. Event b= motionless: is asserted when vehicle becomes stationary. 10. Event c= obstacle detected: is asserted when an obstacle is present in the vehicle's path. 11. Event d= move and avoid: changes the heading and/or speed of the vehicle. 12. Event e= stopped: vehicle stopped. 13. Event f= path finished: is asserted when a given path is completed. 14. Event g= path evaluated: is asserted when a given path is computed. 15. Event h= clear and free the path: is asserted when there is no obstacle in the vehicle's field of view. C) Tracking During the tracking mode, we provide the robot with the capabilities of tracking the map to reach the mine (target). This requires choosing the right features to track and thereby uniquely describing the motion of the target in front. Tracking behavior is modeled as (Fig. 5): 1. Set of states Q = {S 1; S 2} where S 1 stands for Searching ; while S 2 represents Tracking the target. 2. Set of events Σ={a; b} where a = target detected; b= target lost; c = move and follow the track; 3. Where Σ u = {target and goal detected; target lost} 4. Initial state q 0 = {searching} 5. State S 1 = searching. The vehicle is stationary or moving looking for the determined target. 6. State S 2 = tracking the target. The target is in the field of view of the vehicle and the process generates commands to keep the target in the center of the field of view and within a constant distance from the vehicle. 7. Event a= target and goal detected: is asserted when target to be tracked is detected. 8. Event b= target lost: is asserted when target disappears from the field of view. 9. Event c= move and follow the track: is a command to the actuator in order to keep the target in the center of the field of view. V. CONCLUSIONS This paper presented a strategy for the generation of formation trajectories for UGV and UAV. Autonomous take-off, tracking of a UAV and movement of UGV is discussed in this paper. A State-Base Behavioral approach was adopted in the formation manner by using Deterministic Finite Automata. The successful generation and simulation of landmines from UAV are projected on GIS-map. The program has been developed in such a way that it increases efficiency of moving and localization of mobile robot by the help of quadcopter which can be introduced into other related applications for unmanned navigation designs [13]. Our work includes scenarios with obstacles avoidance and the application of the proposed controller experimentally. REFERENCES [1] P. Coelho, U. Nunes, Path-Following Control of Mobile Robots in Presence of Uncertainties, IEEE Transactions on Robotics, Vol. 21, No. 2, April [2] H. Cheng, Y. Chen, X. Li, and W. S. Wong, Autonomous takeoff, tracking and landing of a UAV on a moving UGV using onboard monocular vision, Chinese Control Conference (CCC), 2013, pp [3] R. Chetty, M. Singaperumal, T. Nagarajan, Distributed formation planning and navigation framework for wheeled mobile robots, J. Applied Sci., 2011, 11: [4] M. Dorigo, M. Colombetti, Robot Shaping an Experiment in Behavior Engineering, The MIT Press, Cambridge, MA, 1998 [5] G. Antonelli, F. Arrichiello, and S. Chiaverini, Experiments of formation control with multirobot systems using the null-space-based behavioral control, IEEE Transactions on Control Systems Technology, 17(5): , Sept [6] G. Antonelli, Swarm of robots flocking via the null-space-based behavioral control, 2009 IEEE International Conference on Automation and Logistics, 08/2009 [7] L. Parker, G. Antonelli, F. Caccavale, A Decentralized Architecture for Multi-Robot Systems Based on the Null-Space-Behavioral Control with Application to Multi-Robot Border Patrolling, Journal of Intelligent & Robotic Systems, Moustafa M. Kurdi and Imad Elzein 5

6 UAV AND UGV STATE-BASED BEHAVIORAL FORMATION CONTROL FOR LANDMINE OPERATIONS [8] S. Chiaverini, The null-space-based behavioral control for soccerplaying mobile robots, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2005 [9] R.C. Arkin, Motor schema based mobile robot navigation, The International Journal of Robotics Research, 8(4):92 112, [10] J. Bryson, Action selection and individuation in agent based modelling, Agent 2003: Challenges of Social Simulation, pp , [11] Y. Cao, A. Fukunaga, A. Kanhg, Cooperative mobile robotics: Antecedents and directions, Autonomous Robots, 4:7 27, [12] M. Kurdi, A. Dadykin, I. Elzein, Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV, Communications on Applied Electronics (CAE), vol.6, no.7, pp , February [13] J. Kosecka, R. Bajcsy, Discrete event systems for autonomous mobile agents, Robotics and Autonomous Systems, 12: , [14] J. Carvalho, E. Paiva, J. Ramos, A. Elfes, M. Bergerman, S. Bueno, Air-ground robotic ensembles for cooperative applications: concepts and preliminary results, International Conference on Field and Service Robotics, pages 75 80, Moustafa M. Kurdi and Imad Elzein 6