面向抓取任务的移动机器人停靠位置优化方法研究

为了解决移动机器人在复杂环境中物体抓取规划成功率低以及规划时间长等问题, 本文提出了一种基于环境信息的预处理生成移动机器人停靠位置优化算法。首先对机械臂的工作空间进行分析, 得到抓取难易评价标准, 将环境中目标物、障碍物以及移动底盘位置简化为点, 投影到xy平面上, 根据抓取难易评价标准求出移动机器人优化后的底盘停靠位置; 然后针对机械臂避障问题, 采用快速扩展随机树(Rapidly-exploring Random Trees, RRT)算法实现了机械臂末端及连杆与障碍物的避障; 最后通过仿真和动作捕捉系统下的实验发现, 采用移动机器人停靠位置优化算法可显著提高抓取规划成功率和规划速度。

A Research on Docking Position Optimization Method of Mobile Robot for Grasping Task

In order to solve the problems of low success rate and long planning time of mobile robot in complex environment, a mobile robot docking position optimization algorithm is proposed based on environment information preprocessing. Firstly, the workspace of the manipulator is analyzed, and the evaluation criteria of grasping difficulty are obtained. The positions of objects, obstacles and mobile chassis in the environment are simplified as points and projected on the xy plane. According to the evaluation criteria of grasping difficulty, the optimized chassis docking position of the mobile robot is obtained. For the obstacle avoidance problem of the manipulator, the rapidly-exploring random trees (RRT) algorithm is used to realize the robot end, connecting rod and obstacle. Finally, through the simulation and experiments in the motion capture system, it is found that using the parking position optimization algorithm of mobile robot can increase the success rate of grasping objects and the speed of grasping planning .