摘要
为了减少机器人在车间工作时的路径长度、提高行驶安全性,提出了全局规划和局部滚动避障相结合的机器人导航方法。对车间静态环境进行全局路径规划,在传统蚁群算法基础上,在转移概率中引入节点被访问次数作为新启发因子、同时引进随机选择策略和“回退-惩罚”策略,从而提出了基于改进蚁群算法的全局路径规划方法。对车间动态环境进行局部滚动预测避障,分确定和不确定运动提出了碰撞预测和碰撞避免策略,实现了沿全局最优路径滚动避障行驶。经仿真验证,改进蚁群算法规划出的路径比传统方法缩短了42.3%;在车间动态环境下,机器人使用滚动预测避障策略可以沿着最优路径安全到达目标点,实现了机器人在车间动态环境下安全导航。
To lessen path length of robot in workshop and improve running safety, navigation method combined global planning with local rolling anti-collision is proposed. Global path planning is executed for workshop static environment. On basis of traditional ant colony algorithm, grid visited time is introduced to transferring property, and at the same time, random selection strategy and “rollback-punishment”strategy are introduced. So that global path planning based on improved ant colony algorithm is put forward. Local rolling predicting anti-collision is executed for dynamic environment, collision prediction and avoidance strategies are presented by dividing deterministic and uncertainty movement, which makes robot rolling anti-collision come true. By simulation clarification, length of path planned by improved algorithm decreases by 42.3% than traditional algorithm. Under workshop dynamic environment, robot reach the goal safely belong global optimal path by using rolling prediction anti-collision strategy, which means robot safe navigation in workshop has come true.
作者
谭焓
TAN Han(Wuhan Vocational College of Software and Engineering,Wuhan Hubei 430205,China)
出处
《机械设计与制造》
北大核心
2019年第7期268-272,共5页
Machinery Design & Manufacture
基金
武汉市属高校产学研项目(CXY201620)
关键词
车间动态环境
机器人导航
改进蚁群算法的全局路径规划
局部滚动预测避撞
Workshop Dynamic Environment
Robot Navigation
Path Planning by Improved ant Colony Algorithm
Local Rolling Prediction Anti-Collision
