基于子目标搜索的机器人目标导向RRT路径规划算法

为解决移动机器人未知环境下的路径规划问题,提出基于子目标搜索的机器人目标导向RRT (rapidly-exploring random trees)路径规划算法.一方面,针对传统RRT算法固有的盲目搜索问题,引入目标导向函数,形成目标导向RRT路径规划算法,这一改进可减少冗余搜索,提高路径规划效率;另一方面,为了使机器人在首次探索未知环境时也能顺利抵达目标点,提出3种不同情况下的子目标搜索策略,包括无障碍环境下的直达策略、扫到边界点时的最短距离策略和扫不到边界点时的后退策略,这3种策略使机器人能够完成对未知环境的探索,而且可以克服易出现的局部极小点问题,使机器人具有逃离局部极小环境的能力.仿真实验结果验证了所提出算法的可行性和有效性.

Robot goal guide RRT path planning based on sub-target search

In order to solve the problem of path planning in the unknown environment of mobile robots, a path-planning algorithm of rapidly-exploring random trees(RRT) based on subtarget search is proposed. On the one hand, aiming at the blind search problem inherent in the traditional RRT algorithm, the objective guidance function is introduced to form a goal-directed RRT path planning algorithm, which can decrease redundant search, and improve the efficiency of path planning. On the other hand, in order to enable the robot to reach the target point successfully when it firstly explores the unknown environment, three sub-target search strategies under different circumstances are proposed, which include the direct strategy in the barrier-free environment, the shortest distance strategy when the boundary point is swept, and the backward strategy when the boundary point is not swept. They make the robot can complete exploration of the unknown environment, and, more importantly, can overcome the local minimum point of the problem. Simulation results verify the feasibility and effectiveness of the proposed algorithm.