| 蒋林,赵杰,闫继宏,陈新元.基于APFIGA的全方位机器人多障碍区路径规划研究[J].高技术通讯(中文),2012,22(1):68~73 |
| 基于APFIGA的全方位机器人多障碍区路径规划研究 |
| Path planning in multiple obstacle areas for omni directional mobile manipulators based on the APFIGA |
| 修订日期：2010-09-19 |
| DOI： |
| 中文关键词: 全方位移动操作机器人， 由遗传算法改进的人工势场法（APFIGA）， 穿越系数， 人工势场（APF）， 遗传算法 |
| 英文关键词: omni directional mobile manipulator, artificial potential field method improved by the genetic algorithm (APFIGA), traversing coefficient, artificial potential field (APF), genetic algorithm |
| 基金项目:863计划(2006AA04Z245)和冶金装备及其控制教育部重点实验室开放基金(2009A03)资助项目 |
| 作者 | 单位 | | 蒋林 | 武汉科技大学冶金装备及其控制省部共建教育部重点实验室 | | 赵杰 | 哈尔滨工业大学机器人技术与系统国家重点实验室 | | 闫继宏 | 哈尔滨工业大学机器人技术与系统国家重点实验室 | | 陈新元 | 武汉科技大学冶金装备及其控制省部共建教育部重点实验室 |
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| 中文摘要: |
| 为解决机器人在多障碍区的路径规划问题，提出了一种基于由遗传算法改进的人工势场法（APFIGA）的全方位移动操作机器人路径规划方法。该规划方法通过在可穿越的障碍区添加障碍物穿越系数来鼓励机器人走捷径，进而提高其路径优化能力；利用当前点邻域内势场强度信息，结合遗传算法确定全方位移动操作机器人的运动方向及速度，并引入机器人速度及移动障碍物速度的影响，得出势场强度下降最快的路径，提高方法的动态环境规划能力。该方法能克服机器人在障碍物附近易于抖动的现象，通过在斥力势中添加与目标距离成正比的系数项解决目标不可达问题， |
| 英文摘要: |
| A method for the path planning for an omni directional mobile manipulator based on the artificial potential field method improved by the genetic algorithm (APFIGA) is proposed to solve the difficuties in robot path planning in a multiple obstacle area. The method uses the traversing coefficient to encourage a robot to traverse a traversable multiple obstacle area. Thus the path planning ability can be improved. And it can obtain a path in which the potential field strength decreases fast by using the manipulator’s neighborhood potential field strength information, and the movement direction and the speed of the manipulator is determined by using the genetic algorithm. Due to the introducing of the effect of robot speed and the mobile obstacle speed, the path planning ability under dynamic environments is improved. The problem that the robot is easy to shake around obstacles is solved. The target point unreachable problem is solved by adding the coefficient item proportional to target distance to the repulsion potential. The local minimum value problem is solved by adding the additional potential field to fill local minimum. The results of simulation and physical experiment verified that the proposed path planning method was correct and effective. |
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