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

This paper proposes a control strategy based on forward reachable sets (FRSs) analysis for multiple pursuers to capture an evader with a higher maneuverability than the pursuers. The strategy first finds the pursuers' capture states at which their FRSs cover the evader's entire FRS so as to improve the possibility of capture. The pursuers approach these capture states from their initial states in feed-forward control at a low sampling rate. Upon reaching the capture states, the pursuers approach and capture the evader in a feedback manner. The proposed strategy was applied to two pursuit-evasion scenarios and compared to the generic motion tracking algorithm. Results show the efficacy of the proposed strategy and its superiority to the motion tracking algorithm