Abstract

This paper presents an algorithm which extends the rapidly-exploring random tree (RRT) framework to deal with change of the task environments. This algorithm called the Retrieval RRT Strategy (RRS) combines a support vector machine (SVM) and RRT and plans the robot motion in the presence of the change of the surrounding environment. This algorithm consists of two levels. At the first level, the SVM is built and selects a proper path from the bank of RRTs for a given environment. At the second level, a real path is planned by the RRT planners for the given environment. The suggested method is applied to the control of KUKA™, a commercial 6 DOF robot manipulator, and its feasibility and efficiency are demonstrated via the cosimulatation of MatLab™ and RecurDyn™.

How Multibody Dynamics Simulation Technology is Used

Through co-simulation of RecurDyn and Simulink robot motion can be intelligently planned in a changing environment. RecurDyn can be used to change the environment around the robot to ensure the control algorithm is robust for a large number of environments.

Get This Paper

• http://www.dbpia.co.kr/Journal/ArticleDetail/856624

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