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Creators/Authors contains: "Joshi, Sagar Suhas"

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  1. ; ; (, IEEE)
    Sampling-based algorithms solve the path planning problem by generating random samples in the search space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples so as to improve the cost-to-come value of vertices in a given neighborhood. The application of the proposed algorithm adds an exploitative bias to sampling and results in a faster convergence1 to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed for 7 DOF Panda and 14 DOF Baxter robots. 
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  2. ; ; (, IEEE Robotics and Automation Letters)
    null (Ed.)
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  3. ; (, International Conference on Intelligent Robots and Systems)
    null (Ed.)
    Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for improvement is to generate new samples in the so-called “Informed Set”. However, Informed Sampling can be ineffective in focusing search if the chosen heuristic fails to provide a good estimate of the solution cost. This work proposes an algorithm to sample the “Relevant Region” instead, which is a subset of the Informed Set. The Relevant Region utilizes cost-to-come information from the planner’s tree structure, reduces dependence on the heuristic, and further focuses the search. Benchmarking tests in uniform and general cost-space settings demonstrate the efficacy of Relevant Region sampling. 
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