| 引用本文: | 周自维,李长乐,赵杰,徐望宝.复杂局部地形中的实时路径规划算法设计[J].哈尔滨工业大学学报,2014,46(8):65.DOI:10.11918/j.issn.0367-6234.2014.08.011 |
| ZHOU Ziwei,LI Changle,ZHAO Jie,XU Wangbao.A real time path planning algorithm based on local complicated environment[J].Journal of Harbin Institute of Technology,2014,46(8):65.DOI:10.11918/j.issn.0367-6234.2014.08.011 |
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| 摘要: |
| 针对复杂局部环境中机器人实时自主导航问题,设计了“双向搜索多边形构造算法”和“基于势场函数的机器人运动控制器”．“双向搜索多边形构造算法”能够在机器人被障碍物包围的环境下搜索出障碍物的包围多边形,从而获取基于障碍的最优行进路径;“基于势场函数的机器人运动控制器”是一个多变量控制器,输入矢量由吸引势场函数和排斥势场函数组成,输出矢量由速度和转角组成,该控制器控制机器人实际运动,使机器人能够有效躲避障碍物并逐步趋向目标点;控制器还设定了机器人运动的基本速度,解决势场为零时引起的局部极小化问题．与“沿墙走算法”、“人工势场法”等方法的实验比较表明,本文算法能够获得更好的优化性和实时性,具有更加广泛的实际应用范围． |
| 关键词: 路径规划 局部最优 运动控制器 自主导航 沿墙走算法 |
| DOI：10.11918/j.issn.0367-6234.2014.08.011 |
| 分类号:TH137 |
| 基金项目:国家自然科学基金资助项目 (51105101). |
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| A real time path planning algorithm based on local complicated environment |
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ZHOU Ziwei1,2, LI Changle1, ZHAO Jie1, XU Wangbao2
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(1. State Key Laboratory of Robotic and System,Harbin Institute of Technology, 150001 Harbin, China; 2. School of Electronics ＆ Information Engineering, Liaoning University of Science and Technology, 114000 Anshan, Liaoning,China)
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| Abstract: |
| A novel algorithm, which comprises with convex hull construction algorithm and robot controller is proposed for robot path planning based on complicated local data in robot’s autonomous navigation system. First the algorithm searches out the local optimal path from the robot’s current position to its target according to the local obstacle data. When the robot can not reach the final target directly, a temporary target point in the optimal path will be set to instruct the robot to avoid the obstacle and reach the final target. Next, a controller is design based on attractive force field and repulsive force field to control the robot’s motion, the combined effect of both attractive force field and repulsive force field drives the robot move toward the objective acquired from the optimal path and avoid obstacles at the same time. The experiment results show that this method can provide a better planning path compared with traditional path planning algorithms such as artificial potential field (APF), the wall-following (Bug) and the artificial moment method, and it has a fast reaction speed that is suitable for practical applications. |
| Key words: path planning local optimal motion controller autonomous navigation wall-following |
