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路径规划作为移动机器人顺利完成作业任务的前提,成为了机器人控制领域的研究重点和热点问题。人工势场法以其简洁性和有效性在路径规划中普遍应用,然而由于移动机器人对周围环境信息感知的局限性,容易导致局部极小问题的出现。针对此问题,提出了解决路径规划局部极小问题的势场栅格法。首先对机器人的工作环境进行栅格划分,然后应用改进后的人工势场法为每个栅格赋予势场值,机器人通过搜索势场值的下降方向不断接近目标点,接着采用赋最大值法对局部极小区域的栅格重新赋值,降低搜索的盲目性,使机器人以最优路径到达目标地点。对相同环境下的传统算法与改进算法进行仿真实验对比,结果表明无论是在有、无极小区域,或是目标点在障碍物附近,改进后的算法均可以成功规划出路径,且有效解决了传统人工势场法所面临的局部极小问题。
As a prerequisite for the successful completion of homework tasks of mobile robots, path planning has become a research focus and hot topic in the field of robot control. The artificial potential field method is widely used in path planning because of its conciseness and validity. However, due to the limitations of mobile robot’s perception of the surrounding environment information, it is easy to cause the emergence of local minimum problems. Aiming at this problem, a potential field grid method is proposed to solve the local minimum problem of path planning. Firstly, the working environment of the robot is divided into grids, and then the potential field value is given to each grid by the improved artificial potential field method. The robot continuously approaches the target point by the descent direction of the search potential field value, and then adopts the maximum value method Redistribution of the grid in a local minima reduces the blindness of the search and enables the robot to reach the target location with the optimal route. Compared with the traditional algorithm and the improved algorithm under the same environment, the simulation results show that the improved algorithm can successfully plan out the paths both in the area with or without minutiae and the target point near the obstacle, and effectively solve the problem The Local Minimal Problem Faced by Traditional Artificial Potential Field.