摘要
快速探索随机树^*(RRT^*)作为一种渐进最优的采样运动规划方法在探索高维空间中具有显著的优势。但RRT^*在执行时需要进行大量的碰撞检测,导致其在复杂环境中收敛速度较慢,效率较低。因此,提出了一种无碰撞检测RRT^*运动规划方法。该方法剔除了RRT^*扩展时的碰撞检测,并在代价函数中增加碰撞风险评估函数,从而影响各个节点对父节点的连接选择。当节点或边与障碍物碰撞时,碰撞风险评估函数将显著增大,促使代价函数值显著增大,同时通过设计路径代价的上限,保证了运动规划的避碰能力。最后,仿真结果表明所提方法的收敛速度比RRT^*提高约40%,且在复杂环境中优势更加明显。同时通过移动机器人真实环境实验测试验证了所提方法实时规划的可行性。
The rapidly-exploring random tree^*(RRT^*)is an asymptotic optimal sampling-based motion planning method,which has advantages of exploring high-dimensional space.However,it needs to implement lots of collision checking during each iteration of the RRT^*.This results in the slow convergence speed and low efficiency in complex environments.To address these issues,this paper proposes a non-collision checking RRT^*motion planning method.The collision checking operation in RRT^*extension can be eliminated,and the collision risk assessment function in the cost function is added.The parent node connecting selection of each node is affected.When a node or edge collides with obstacles,the collision risk assessment function will increase significantly.In this way,the cost function value is increased synchronously.Meanwhile,the upper bound of the path cost is designed to ensure collision avoidance ability of motion planning.Finally,simulation results show that the proposed method has faster convergence speed,which is about 40%higher than RRT^*.More advantages can be found in complex environments.The real-time planning ability of the proposed method is evaluated by the experiment of the mobile robot in the real environment.
作者
林依凡
陈彦杰
何炳蔚
黄益斌
王耀南
Lin Yifan;Chen Yanjie;He Bingwei;Huang Yibin;Wang Yaonan(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;National Engineering Laboratory for Robot Visual Perception and Control Technology,Changsha 410082,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第10期257-267,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61803089)
福建省自然科学基金(2019J01213)
福建省中青年教师教育科研项目(JT180024)资助
关键词
采样运动规划
避障
快速探索随机树^*
移动机器人
sampling-based motion planning
obstacle avoidance
rapidly-exploring random tree^*
mobile robot
作者简介
林依凡,2018年于福州大学获得学士学位,现为福州大学硕士研究生,主要研究方向为移动机器人路径规划。E-mail:1040365120@qq.com;通讯作者:陈彦杰,2011年于西南交通大学获得学士学位,2013年于湖南大学获得硕士学位,2017年于湖南大学获得博士学位,现为福州大学机械工程及自动化学院助理教授,主要研究方向为机器人动力学控制、运动规划方法。E-mail:chenyanjie@fzu.edu.cn
