Path Planning with Obstacle Avoidance in PEGs: Ant Colony Optimization Method

This paper is concerned with the problem of path planning in discrete-time pursuit-evasion games (PEGs) with obstacles. We present the so-called I-ACO (Improved Ant Colony Optimization) algorithm to plan paths for pursuers with efficiency and collision avoidance. The I-ACO algorithm can be divided into two modes, i.e. Approaching Mode and Capturing Mode, according to whether the evaders are sensed by the pursuers. The ant strategy provides the path planning methods with Direction Factor to keep the pursuers tracking the evaders. Furthermore, a blocking rule is presented to prevent the virtual ants from moving to a place repeatedly. Moreover, we present a Smoothening Rule to shorten the path found by the virtual ants. In the simulation, the path to the evader was simulated by using virtual ants. The simulation results show the high efficiency of the I-ACO algorithm.