Real-Time Path Planning Algorithm for Autonomous Border Patrol: Design, Simulation, and Experimentation

This paper presents an online path planning algorithm for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle, acting as the pursuer, is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs’ Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

[1]  João Pedro Hespanha,et al.  On Discrete-Time Pursuit-Evasion Games With Sensing Limitations , 2008, IEEE Transactions on Robotics.

[2]  Emilio Frazzoli,et al.  Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games , 2010, WAFR.

[3]  Narendra Ahuja,et al.  A potential field approach to path planning , 1992, IEEE Trans. Robotics Autom..

[4]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[6]  Liang Tang,et al.  Hierarchical decomposition approach for pursuit-evasion differential game with multiple players , 2006, 2006 IEEE Aerospace Conference.

[7]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[8]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[9]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[10]  Emilio Frazzoli,et al.  Incremental Sampling-based Algorithms for Optimal Motion Planning , 2010, Robotics: Science and Systems.

[11]  Weihai Zhang,et al.  Infinite time nonzero-sum linear quadratic stochastic differential games , 2010, Proceedings of the 29th Chinese Control Conference.

[12]  Jonathan P. How,et al.  Real-Time Motion Planning With Applications to Autonomous Urban Driving , 2009, IEEE Transactions on Control Systems Technology.

[13]  Mario Fernando Montenegro Campos,et al.  On the Generation of Trajectories for Multiple UAVs in Environments with Obstacles , 2010, J. Intell. Robotic Syst..

[14]  Donald E. Kirk,et al.  Optimal control theory : an introduction , 1970 .

[15]  William W. Hager,et al.  Pseudospectral methods for solving infinite-horizon optimal control problems , 2011, Autom..

[16]  E. Feron,et al.  Real-time motion planning for agile autonomous vehicles , 2000, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[17]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[18]  Jose B. Cruz,et al.  A two-player stochastic pursuit-evasion differential game , 2007, 2007 46th IEEE Conference on Decision and Control.

[19]  Tal Shima,et al.  Bounded differential games guidance law for dual-controlled missiles , 2006, IEEE Transactions on Control Systems Technology.

[20]  S. Shankar Sastry,et al.  Pursuit-evasion strategies for teams of multiple agents with incomplete information , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[21]  George Lau.  Online Border Patrol Guidance Algorithm for Unmanned Aerial Vehicles , 2012 .

[22]  P. Varaiya,et al.  Differential games , 1971 .

[23]  Dongxu Li,et al.  A Hierarchical Approach To Multi-Player Pursuit-Evasion Differential Games , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.