摘要
为了使机器人在复杂动态环境中实现最优路径规划,提出了改进人工蜂群算法的路径规划方法。分析了传统的人工蜂群算法原理,引入了小步长侦查蜂为跟随蜂提供障碍物分布的先验信息;为了防止机器人与动态障碍物发生碰撞,对动态障碍物周围的可行节点进行障碍化处理,将节点障碍指数与节点与目标点距离糅合为节点选择准则,同时提出了障碍避撞预测和障碍避撞策略。由仿真实验可以看出,改进算法不仅成功实现避撞,而且规划出避撞条件下的最优路径。
To plan optimal path for robot under complex dynamic environment, path planning based on improved artificial bee colony algorithm is proposed, Principle of traditional artificial bee colony algorithm is analyzed. Short step scout bee is imported to detect obstacles condition in every possible forward direction of following bee, which is prior information for following bee to choose forward direction. In order to prevent collision of robot and dynamic barrier, feasible grids around dynamic barrier are treated to some degree of barrier. Grid barrier index and distance of the grid and goal are mixed as grid choice criteria Anti-collision prediction and anti-collision strategy are put forward. Simulation experiment is executed to clarify the algorithm, the improved algorithm can not only prevent collision successfully, but it also can plan a optimal path.
出处
《机械设计与制造》
北大核心
2017年第11期255-258,共4页
Machinery Design & Manufacture
基金
国家自然科学基金(61272470)
上海第二工业大学校级重点学科建设资助(NO:XXKPY1603)
关键词
机器人避撞路径规划
动态未知环境
避撞策略
节点障碍指数
Robot Anti-Collision Path Planning
Dynamic Unknown Environment
Anti-Collision Strategy
Grid Bar-tier Index
