期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2017
卷号：14
期号：3
DOI：10.1177/1729881417703930
语种：English
出版社：SAGE Publications
摘要：Traditional manipulator motion planning methods aim to find collision-free paths. But in highly cluttered environments, it is hard to find available solutions. We present a novel motion planning strategy which integrates the sampling-based path planning algorithm with the flexible obstacle avoidance approach for finding the efficient path through changing poses of movable obstacles. Following the resulting path, the manipulator can push the obstacles away and move to the target simultaneously. For dealing with the safety issue of the interaction between manipulator and obstacles, a learning-based motion modeling method is proposed for motion prediction of the obstacles being pushed by manipulator, and then the trained models are utilized in the motion planning. The results from both simulations and real robot experiments show that the proposed method can generate efficient paths which cannot be solved by traditional method.
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