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During the past decade, Rapidly-exploring Random Tree (RRT) and its variants are shown to be powerful sampling based single query path planning approaches for robots in high-dimensional configuration space. However, the performance of such tree-based planners that rely on uniform sampling strategy degrades significantly when narrow passages are contained in the configuration space. Given the assumption...
Path planning for robots with many degrees of freedom (dof) receives continuous interest in both robotics and computer graphics communities. A variety of random sampling-based methods have been proposed to solve the path planning problems in high-dimensional configuration space, including Probabilistic Roadmap Method (PRM) and Rapidly-exploring Random Trees (RRTs). However, the efficiency will be...
A new classification of robot cognitive models is presented in terms of knowledge representation, which clarifies the importance of tacit knowledge in robot cognitive model research. A robot cognitive model based on procedural memory and episodic memory is proposed, and every module in this cognitive model is designed in detail. High-level intelligent behaviors can emerge totally by using experiences...
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