Biologically Inspired Node Generation Algorithm for Path Planning of Hyper-redundant Manipulators Using Probabilistic Roadmap

查看全文 作　　者：Eric [1]Lanteigne;Amor [2]Jnifene

机构地区：[1]Department of Mechanical Engineering,University of Ottawa;[2]Department of Mechanical and Aerospace Engineering,Royal Military College of Canada

出　　处：《International Journal of Automation and computing》索引2014年第11卷第2期,共9页

摘　　要：This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator. 关键词：Path planning hyper-redundant MANIPULATORS PROBABILISTIC road map(PRM) quasi-deterministic NODE generation bi-directional search algorithm.

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Biologically Inspired Node Generation Algorithm for Path Planning of Hyper-redundant Manipulators Using Probabilistic Roadmap

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