基于栅格法的机器人路径规划快速搜索随机树算法被引量：21

Rapid-Exploring Random Tree Algorithm for Path Planning of Robot Based on Grid Method

下载PDF

导出

摘要针对复杂环境下的机器人路径规划问题,提出了一种全新的基于栅格法的机器人路径规划快速搜索随机树算法.以机器人出发点为随机树的根节点,通过扩展,逐渐增加叶节点直至随机树的叶节点中包含了目标点.从出发点到目标点之间的一条以随机树的边组成的路径就是目标路径.研究表明在同样的环境下与遗传算法、A*算法相比该方法能在更短的时间内找到更优的路径.仿真实验也表明,即使在随机生成的复杂环境下,利用该算法也可以快速规划出一条全局优化路径,且能安全避障. A new rapid-exploring random tree algorithm for path planning of robot based on grid method is proposed to plan an optimal path for mobile robot in complex environment. The algorithm explores the space and add a new node to a random tree in which that root node is the position of robot until the leaf node of the tree contains the goal node. The path composed from the edges of the initial node to the goal node is where the robot walk by. It is tested that the proposed algorithm is more effective than GA and A^ ＊ under the same environment. The simulation results illustrate that the proposed algorithm can be used to solve the path planning for mobile robot even in the random complex environment the robot can avoid the obstacles safely by the path gained by the new algorithm.

作者国海涛朱庆保徐守江

机构地区南京师范大学数学与计算机科学学院

出处《南京师范大学学报（工程技术版）》 CAS 2007年第2期58-61,共4页 Journal of Nanjing Normal University(Engineering and Technology Edition)

基金国家自然科学基金(60673102) 江苏省自然科学基金(BK2006218)资助项目

关键词机器人路径规划快速搜索随机树栅格法 robot, path planning, rapidly-exploring random tree, grid method

分类号 TP24 [自动化与计算机技术—检测技术与自动化装置]

引文网络
相关文献

参考文献7

1[1]Nearchou A C.Path planning of a mobile robot using genetic heuristics[J].Robotica,1998,16(5):575-588.
2[4]D Amico A,Ippoliti G,Longhi S.A radial basis function networks approach for the tracking problem of mobile robots[C]//Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics.Como,Italy,2001,1:498-503.
3[5]Allan R Willms,Simon X.Neural network approaches to dynamic collision-free robot trajectory generation[J].IEEE Trans Syst Man,Cybern,2001,31(3):302-318.
4[6]Yang S X,Max M.An efficient neural network approach to dynamic robot motion planning[J].Neural Networks,2000,13(2):143-148.
5[7]Karen I Trovato,Leo Dorst.Differential A*[J].IEEE Transactions on Knowledge and Data Engineering,2002,14(6):1218-1229.
6[8]Bruce J,Veloso M.Real-time randomized path planning for robot navigation[C]//Proceedings for the IEEE/RSJ International Conference on Intelligent Robots and System.EPFL,Lausanne,Switzerland,2002,3:2383-2388.
7[9]Peng Cheng,Steven M LaValle.Resolution complete rapidly-exploring random trees[C]//IEEE International Conference on Robotics and Automation.Washington,2002:267-272.