An intelligent suboptimal path planning algorithm using Rapidly-exploring Random Trees

In this paper, we present an algorithm using Rapidly-exploring Random Trees (RRTs) to generate suboptimal paths for unmanned air vehicles (UAVs) in real time, given a starting location and a goal configuration in the presence of both static and pop-up obstacles. Generating near optimal paths in obstacle rich environments within a given short time window is a challenging task. Hence we first generate a path quickly using RRT by taking the kinematic constraints of the UAV into account. Then we develop an anytime algorithm that yields paths whose quality improves as execution time increases. When the UAV detects a pop-up obstacle the path planner regenerates a new path from its next “safe” location. In order to track a generated path, an effective guidance law based on pursuit and line of sight guidance laws is developed. Simulation studies are included to show the performance of the proposed algorithm.