基于多优化快速扩展随机树算法的足球机器人路径规划

研究RoboCup比赛未知环境中足球机器人的路径规划问题。提出一种多优化设计快速扩展随机树(rapidly exploring random tree,RRT)的足球机器人路径规划算法,解决了足球机器人在路径规划中存在的速度慢、效果差的问题。依据基本RRT算法原理,针对其随机性强、收敛速度慢以及路径平滑效果差的缺点,提出了随机采样点处增加引力分量、多步扩展逼近目标点以及冗余节点的剪裁与路径平滑等优化方式。在不同障碍物地图中的仿真实验表明,优化的规划路径长度比基本快速扩展随机树算法所得路径缩短约20%～30%,采样点数量减少45%～65%。最终将优化算法移植到SimRobot仿真平台,结果表明多优化设计RRT算法在未知环境中具备良好的实时性和鲁棒性,能够满足机器人在赛场上的路径规划要求。

Path Planning Based on Multi-optimization Design RRT Algorithmfor Soccer Robot

path planning problem for soccer robots in the unknown environment of RoboCup competition is researched. This paper proposes a MOD-RRT soccer robot path planning algorithm, which solves the problem that the soccer robot has slow speed and poor effect in path planning. According to the principle of basic RRT algorithm, aiming at the shortcomings of strong randomness, slow convergence and poor path smoothing effect, optimization methods such as increasing the gravitational component at random sampling points, multi-step extended approximation target points and tailoring and path smoothing of redundant nodes are proposed. The simulation experiments in different obstacle maps show that the optimized planning path length is reduced by about 20%~30%,the number of sampling nodes is reduced by about 45%~65% compared with the basic rapidly-exploring random tree algorithm. Finally, the optimization algorithm is transplanted to the SimRobot simulation platform. The results show that the MOD-RRT algorithm has good real-time and robustness in the unknown environment, which can meet the path planning requirements of the robot on the competition.