Swarm Coordination for Pursuit Evasion Games using Sensor Networks

In this work we consider the problem of pursuit evasion games (PEGs) where a group of pursuers is required to detect, chase and capture a group of evaders with the aid of a sensor network in minimum time. Differently from standards PEGs where the environment and the location of evaders is unknown and a probabilistic map is built based on the pursuer’s onboard sensors, here we consider a scenario where a sensor network, previously deployed in the region of concern, can detect the presence of moving vehicles and can relay this information to the pursuers. Here we propose a general framework for the design of a hierarchical control architecture that exploits the advantages of a sensor network by combining both centralized and decentralized real-time control algorithms. We also propose a coordination scheme for the pursuers to minimize the time-to-capture of all evaders. In particular, we focus on PEGs with sensor networks orbiting in space for artificial space debris detection and removal.

[1]  S. Sastry,et al.  Decentralized Reflective Model Predictive Control of Multiple Flying Robots in Dynamic Environment , 2022 .

[2]  J. B. Collins,et al.  Efficient gating in data association with multivariate Gaussian distributed states , 1992 .

[3]  Rainer E. Burkard,et al.  Linear Assignment Problems and Extensions , 1999, Handbook of Combinatorial Optimization.

[4]  J.P. Hespanha,et al.  Multiple-agent probabilistic pursuit-evasion games , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[5]  Nicholas L. Johnson,et al.  Monitoring and Controlling Debris in Space , 1998 .

[6]  Bruno Sinopoli,et al.  Distributed control applications within sensor networks , 2003, Proc. IEEE.

[7]  Ingemar J. Cox,et al.  A review of statistical data association techniques for motion correspondence , 1993, International Journal of Computer Vision.

[8]  Roberto Zanasi,et al.  Discrete minimum time tracking problem for a chain of three integrators with bounded input , 2003, Autom..

[9]  S. Shankar Sastry,et al.  A Hierarchical Multiple-Target Tracking Algorithm for Sensor Networks , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Xiaotie Deng,et al.  How to learn an unknown environment. I: the rectilinear case , 1998, JACM.

[11]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[12]  Miodrag Potkonjak,et al.  Exposure in wireless Ad-Hoc sensor networks , 2001, MobiCom '01.

[13]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[14]  Zhiqiang Gao,et al.  On discrete time optimal control: a closed-form solution , 2004, Proceedings of the 2004 American Control Conference.

[15]  Feng Zhao,et al.  Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications , 2003, IPSN.

[16]  Gregory J. Pottie,et al.  Instrumenting the world with wireless sensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[17]  Feng Zhao,et al.  Distributed state representation for tracking problems in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[18]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[19]  Aubrey B. Poore,et al.  Multidimensional Assignment Problems Arising in Multitarget and Multisensor Tracking , 2000 .

[20]  L. El Ghaoui,et al.  Algorithms for air traffic flow management under stochastic environments , 2004, Proceedings of the 2004 American Control Conference.

[21]  David E. Culler,et al.  Sensor Field Localization: A Deployment and Empirical Analysis , 2004 .

[22]  René Vidal,et al.  A hierarchical approach to probabilistic pursuit-evasion games with unmanned ground and aerial vehicles , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).