Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields

• URL: http://arxiv.org/abs/2409.04306v1
• Date: Fri, 6 Sep 2024 14:28:41 GMT
• Title: Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields
• Authors: Felix Herrmann, Sebastian Zach, Jacopo Banfi, Jan Peters, Georgia Chalvatzaki, Davide Tateo,
• Abstract summary: Estimating collision probabilities is crucial to ensure safety during path planning. Deep Collision Probability Fields is a neural-based approach for computing collision probabilities of arbitrary objects. Our approach relegates the computationally intensive estimation of collision probabilities via sampling at the training step.
• Score: 21.741354016294476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating collision probabilities between robots and environmental obstacles or other moving agents is crucial to ensure safety during path planning. This is an important building block of modern planning algorithms in many application scenarios such as autonomous driving, where noisy sensors perceive obstacles. While many approaches exist, they either provide too conservative estimates of the collision probabilities or are computationally intensive due to their sampling-based nature. To deal with these issues, we introduce Deep Collision Probability Fields, a neural-based approach for computing collision probabilities of arbitrary objects with arbitrary unimodal uncertainty distributions. Our approach relegates the computationally intensive estimation of collision probabilities via sampling at the training step, allowing for fast neural network inference of the constraints during planning. In extensive experiments, we show that Deep Collision Probability Fields can produce reasonably accurate collision probabilities (up to 10^{-3}) for planning and that our approach can be easily plugged into standard path planning approaches to plan safe paths on 2-D maps containing uncertain static and dynamic obstacles. Additional material, code, and videos are available at https://sites.google.com/view/ral-dcpf.

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