摘要
基于捡球机器人视野范围有限的特点,采用分层规划思想对全局进行窗口划分,在每个窗口内采用蚁群算法对窗口内球体进行多目标路径规划,Matlab仿真结果表明在机器人搜索完全局后虽不能实现全局最优,但接近全局最优且对捡球机器人的特点具有较强适用性。针对捡球机器人工作动态环境,在传统人工势场法基础上引入机器人与障碍物的相对速度因素,对势场力角度进行调整,并加入自适应步长。仿真结果表明,上述算法解决了机器人在特殊位置关系下不可到达目标点和避障轨迹不平滑问题,使机器人更快摆脱障碍物的影响。
Based on the limited field of vision of ball-picking robot, a hierarchical planning idea was used to divide the global window, in each window, and ant colony algorithm was adopted to carry out multi-objective path planning for the balls in the Windows. Matlab simulation results show that although the global optimization cannot be achieved after the robot search is complete, it is close to the global optimization and has strong applicability to the characteristics of the ball-picking robot. Aiming at dynamic working environment of ball-picking robot, based on the traditional artificial potential field method, the relative velocity between robot and obstacle was introduced, the angle of potential field force was adjusted, and the adaptive step size was added. Matlab simulation results show that this algorithm can solve the problem of inaccessible target point of robot under special position relationship and the problem that the obstacle avoidance trajectory is not smooth, and the robots can get rid of obstacles faster.
作者
陈卫
邓志良
CHEN Wei;DENG Zhi-liang(School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212003,China)
出处
《计算机仿真》
北大核心
2020年第10期291-296,共6页
Computer Simulation
基金
国家自然科学基金项目(61502240,61502096,61304205,61203316,61663027)
江苏省自然科学基金项目(BK20141002,BK20150634)。
关键词
捡球机器人
路径规划
蚁群算法
人工势场法
Ball-picking robot
Path planning
Ant colony algorithm
Artificial potential field method
