基于改进A^(*)算法与动态窗口法融合的机器人随机避障方法研究

Research on robot random obstacle avoidance method based on fusion of improved A algorithm and dynamic window method

针对机器人在存在随机障碍物环境中采用A^(*)算法规划路径会出现碰撞或路径规划失败的问题,提出了一种将改进A^(*)算法与动态窗口法相融合的机器人随机避障方法。在改进A^(*)算法中,首先优化了搜索点选取策略和评价函数,提高了A^(*)算法的搜索效率;然后提出冗余点删除策略,剔除路径中的冗余节点,并在每两个相邻节点间采用动态窗口法进行局部规划,确保在全局最优路径基础之上,实时随机避障,使机器人顺利到达目标点。实验结果表明,改进A^(*)算法较传统A^(*)算法平均可减少4.39%的路径长度和65.56%的计算时长,融合动态窗口法后,能在全局路径基础上修正局部路径,实现随机避障,验证了该算法的有效性。

Aiming at the problems of collision or failure of path planning when the robot uses the A^(*) algorithm to plan a path in the environment with random obstacles, a random obstacle avoidance method for robots that combines the improved A^(*) algorithm with the dynamic window method is proposed. In the improved A^(*) algorithm, firstly, the search point selection strategy and the evaluation function are optimized to improve the search efficiency of the A^(*) algorithm, then the redundant point deletion strategy is proposed to eliminate the redundant nodes in the path, and the dynamic window method is used for the local planning between every two adjacent nodes to ensure that on the basis of the global optimal path, random obstacle avoidance is achieved in real time, so that the robot can reach the target point successfully. The experiment results show that the improved A^(*) algorithm proposed in this paper can reduce the path length by 4.39% and the calculation time by 65.56% on average compared with the traditional A^(*) algorithm. After fusing the dynamic window method, on the global path basis the local path can be modified to achieve random obstacle avoidance, which verifies the effectiveness of the proposed algorithm.