动态环境下基于自适应步长Informed-RRT*和人工势场法的机器人混合路径规划

为解决移动机器人在动态环境下的路径规划问题,将Informed-RRT*和人工势场法相融合,提出全局与局部规划算法相融合的路径规划方法。首先,针对Informed-RRT*算法采样效率低,以及得到路径不满足机器人运动学约束的问题,采用目标偏置法与自适应步长法,减少冗余搜索与不必要树的生长;同时,引入走廊优化与时间重分配法,优化路径节点,使路径更加平滑。其次,针对人工势场法易陷入局部极小值和目标点附近不可达的问题,采用平滑窗格策略,增设全局路径子目标点,使机器人能够逃离局部极小值,完成规划任务。仿真结果表明,静态环境中自适应步长Informed-RRT*算法相比于Informed-RRT*算法求解时间缩短了71.98%;动态环境中,混合算法相比于人工势场法,搜索时间缩短了15.4%,路径长度缩短了11.1%。

Adaptive Step Size Informed-RRT* and Artificial Potential Field Algorithm for Hybrid Path Planning of Robot

In order to solve the path planning problem of mobile robot in the dynamic environment, a hybird strategy based on the global and local algorithms is proposed by combining the Informed RRT* and artificial potential field method. Firstly, the target offset method and adaptive step size method are designed in the process of informed RRT* random point selection to reduce the redundant search and unnecessary tree growth. Corridor optimization and time redistribution method are introduced to optimize the nodes of path for making the path more smooth.Secondly, the artificial potential field method based on subtarget points is employed for the local path planning, and the problem of easy to fall into local minimum and unreachable near the target point for the artificial potential field method is solved. The sub target point is set to make sure that the mobile robot can escape the local minimum and complete the mission. The simulation results show that the time to find a path in a static environment was 71.98% shorter by using adaptive step-length Informed-RRT* algorithm compared to the Informed-RRT* algorithm. Compared to the artificial potential field method,the search time and the path length are decreased 15.4% and 11.1%, respectively, by using the bybird algorithm.