Car-Like Mobile Robot Path Planning in Rough Terrain With Danger Sources

摘要

In many practical robot missions, the workspace may have several danger sources, such as enemy strongholds in the battlefield, explosives in fire rescue missions, and dangerous buildings in earthquake rescue missions. In these situations, the workspace is always rough terrain instead of flat ground. To maximize the safety degree of a path, robots should simultaneously find a relative flat path to the goal position and try to keep a safe distance from the danger sources. In this paper, an existing path planning algorithm based on multi-objective particle swarm optimization is used to find the safe paths with minimum risk degree and terrain roughness. First, the robot working environment is transformed into a matrix which presents the terrain roughness of the workspace using the known static environment data. And the path planning problem converts into a mathematical problem. Considering the effects of danger sources and terrain roughness, a multi-objective optimization algorithm is used to search for feasible paths. Finally, two simulation tests are designed using Microsoft Robotics Developer Studio4 and Matlab. Results show the advantages of the proposed algorithm in finding Pareto optimal paths.