Coordinated Search-and-Capture Using Particle Filters

This paper presents a search-and-capture (SAC) problem where multiple autonomous pursuer vehicles are deployed to capture evaders using the particle filter (PF). The PDFs of the evaders states are first represented as discrete sets of support vectors (particles). Using this representation, a coordinated SAC strategy is proposed by firstly defining the observation likelihoods for both detection and non-detection in the PF framework. Coordination is then achieved through the transmission of the likelihood function parameters of each pursuer to other pursuers to form combined observation likelihoods (COLs) followed by the derivation and sharing of weighted expected states (WESs) from the updated PDFs to provide control reference points for the pursuers. The proposed strategy is applied to two scenarios: first to multiple-pursuers single-evader and secondly to multiple-pursuer multiple-evaders. Results show that the proposed strategy allows the pursuers to successfully detect and capture the evaders in both scenarios

[1]  Hugh F. Durrant-Whyte,et al.  Coordinated decentralized search for a lost target in a Bayesian world , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[2]  Hugh F. Durrant-Whyte,et al.  Recursive Bayesian search-and-tracking using coordinated uavs for lost targets , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[3]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[4]  Hedvig Sidenbladh,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[5]  Sebastian Thrun,et al.  Decentralized Sensor Fusion with Distributed Particle Filters , 2002, UAI.

[6]  A. Doucet.  On sequential Monte Carlo methods for Bayesian filtering , 1998 .

[7]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[8]  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).

[9]  S. Shankar Sastry,et al.  Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation , 2002, IEEE Trans. Robotics Autom..