Multi-robot Cooperative Pursuit Based on Association Rule Data Mining

Abstract

An approach of cooperative hunting for multiple mobile targets by multi-robot is presented, which divides the pursuiting process into forming the pursuiting groups and capturing the targets. The data sets of attribute relationship is built by consulting many factors about capturing evaders, then the interesting rules can be found by data mining from the data sets to build the pursuiting groups. Through doping out the positions of targets, the members of pursuiting can confirm their destinations. Based on these extensions, a kind of multi-robot cooperative pursuit algorithm that allows dynamic alliance is proposed. The simulation results show that the mobile evaders can be captured effectively and efficiently, and prove the feasibility and validity of the given algorithm under dynamic environment.