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为解决机器人在装配、焊接、喷涂等工业生产中的路径规划调试时间长、效率低等问题,提出一种新的基于概率地图的路径搜索优化算法。该算法首先利用混合包围体层次树碰撞检测算法的优势,在每两个采样位姿点间选出无碰撞的局部路径;然后,在获取始末点位姿信息后,引入A*算法搜索全局路径;最后采用提出的路径优化算法对搜索得到的全局路径进行优化。以此实现只需给出运动初始点与目标点,算法就能自动搜索出一条无碰撞的全局优化路径。仿真和机器人实体实验表明该算法适用于工业机器人的路径搜索,所得机器人运动路径简短,并有效地避开障碍物。
In order to solve the problem of long time path planning and low efficiency of robots in assembly, welding, spraying and other industrial production, a new path search optimization algorithm based on probability map is proposed. Firstly, the algorithm takes the advantage of hybrid bounding tree level tree collision detection algorithm and selects the collision-free local path between every two sampling positions and poses. Then, after obtaining the initial and final pose information, A * algorithm is introduced to search the global path Finally, the proposed path optimization algorithm is used to optimize the global path searched. In this way, only the initial point and the target point of the motion are given, the algorithm can automatically search for a collision-free global optimization path. The simulation and robot experiments show that the algorithm is suitable for the path search of industrial robots. The resulting robot has a short path of motion and effectively avoids obstacles.