RRT-based Motion Planning Algorithm for Intelligent Vehicle in Complex Environments

The existing planning algorithms can not properly solve the motion planning problem of intelligent vehicle in complex environments with many irregular and random obstacles. To solve the problem, a simple and practical RRT-based algorithm, continuous-curvature RRT algorithm, is proposed. This algorithm combines the environmental constraints and the constraints of intelligent vehicle with RRTs. Firstly, a goal-biased sampling strategy and a reasonable metric function are introduced to greatly increase the planning speed and quality. And then, a post-processing method based on the maximum curvature constraint is presented to generate a smooth, continuous-curvature and executable trajectory. Simulation experiments and real intelligent vehicle test verify the correctness, validity and practicability of this algorithm.