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NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments
[article]
2022
arXiv
pre-print
Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world
arXiv:2205.06951v1
fatcat:bq6kwbip3vaiffo2wgnrqzh3zq