Path Planning and Obstacle Avoidance for PEGs in WSAN: I-ACO Based Algorithms and Implementation

This paper is concerned with the problem of multi-agent path planning in discrete-time pursuit-evasion games(PEGs). In order to plan paths for the pursuers to capture the evaders and avoid collision under complex environment, we present an improved Ant Colony Optimization (I-ACO) strategy. Compared with the conventional ACO (Ant Colony Optimization) based algorithms, the proposed I-ACO algorithms include the designed Direction Factor, Blocking Rule and Smoothening Rule. It effectively helps to reduce the computational time and shorten the path for the pursuers while keeping tracking. The simulation results and the experiments show the efficiency and the robustness of the improved Ant Colony Optimization algorithm and the elusion of choosing a narrow path.