Multi-Sample Long Range Path Planning under Sensing Uncertainty for Off-Road Autonomous Driving
CoRR(2024)
摘要
We focus on the problem of long-range dynamic replanning for off-road
autonomous vehicles, where a robot plans paths through a previously unobserved
environment while continuously receiving noisy local observations. An effective
approach for planning under sensing uncertainty is determinization, where one
converts a stochastic world into a deterministic one and plans under this
simplification. This makes the planning problem tractable, but the cost of
following the planned path in the real world may be different than in the
determinized world. This causes collisions if the determinized world
optimistically ignores obstacles, or causes unnecessarily long routes if the
determinized world pessimistically imagines more obstacles. We aim to be robust
to uncertainty over potential worlds while still achieving the efficiency
benefits of determinization. We evaluate algorithms for dynamic replanning on a
large real-world dataset of challenging long-range planning problems from the
DARPA RACER program. Our method, Dynamic Replanning via Evaluating and
Aggregating Multiple Samples (DREAMS), outperforms other determinization-based
approaches in terms of combined traversal time and collision cost.
https://sites.google.com/cs.washington.edu/dreams/
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