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Batch Belief Trees for Motion Planning Under Uncertainty
[article]
2021
arXiv
pre-print
In this work, we develop the Batch Belief Trees (BBT) algorithm for motion planning under motion and sensing uncertainties. The algorithm interleaves between batch sampling, building a graph of nominal trajectories in the state space, and searching over the graph to find belief space motion plans. By searching over the graph, BBT finds sophisticated plans that will visit (and revisit) information-rich regions to reduce uncertainty. One of the key benefits of this algorithm is the modified
arXiv:2110.00173v1
fatcat:c7urpkswhzdyjohkvzto7uug7m