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• W2592467019 abstract "Recently, the optimal motion planning problem has attracted a considerable amount of attention, giving rise to new algorithms like RRG, RRT* and PRM*. However, these algorithms have some difficulty in handling the high-dimensional situation like manipulation, which needs a large amount of samples to explore a huge configuration space. In this context, we present a novel incremental sampling-based motion planning algorithm called Fast Convergence Rapidly-exploring Random Tree (FCRRT). Besides the guarantee to asymptotic optimality, our method has two key improvements: (1) the exploration and the optimization procedures are implemented and executed independently to retain the exploration strength of RRT that rapidly grows a random tree toward unexplored regions of the C-space, and (2), the Lazy-RRG technique is used to accelerate the convergence rate of the method. Experimental results indicate that FCRRT significantly improves the exploration rate and success rate, and finds paths of a similar quality much more quickly compared to RRT*." @default.
• W2592467019 created "2017-03-16" @default.
• W2592467019 creator A5057287134 @default.
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• W2592467019 date "2016-12-01" @default.
• W2592467019 modified "2023-09-24" @default.
• W2592467019 title "Fast Convergence RRT for asymptotically-optimal Motion Planning" @default.
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• W2592467019 doi "https://doi.org/10.1109/robio.2016.7866641" @default.
• W2592467019 hasPublicationYear "2016" @default.
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