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

TL;DR

This paper introduces Deep Collision Probability Fields, a neural network-based method for fast and accurate collision probability estimation under uncertainty, improving safety and efficiency in robotic path planning.

Contribution

The paper presents a novel neural approach that replaces sampling-based methods for collision probability estimation, enabling real-time planning under uncertainty.

Findings

Achieves collision probability estimation accuracy up to 10^{-3}

Integrates seamlessly with standard path planning algorithms

Demonstrates effectiveness on 2-D maps with static and dynamic obstacles

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.