用神经网络快速计算机器人碰撞概率，提升路径规划安全与效率
Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields
- 训练时用采样法预估碰撞概率，推理时直接神经网络输出
- 在2D地图上对不确定障碍物实现安全路径规划，误差低至10^{-3}
- 可无缝接入现有规划器，适合自动驾驶等需要实时避障的场景
估计机器人与环境障碍物或其他移动体之间的碰撞概率，对确保路径规划中的安全性至关重要。这在自动驾驶等应用中尤为关键，因传感器噪声导致障碍物感知存在不确定性。现有方法或过于保守，或因基于采样的计算开销大。为此，本文提出深度碰撞概率场（Deep Collision Probability Fields），一种基于神经网络的方法，可计算任意物体在任意单峰不确定性分布下的碰撞概率。该方法将耗时的采样估计环节移至训练阶段，使规划时仅需快速神经网络推理即可获得约束条件。大量实验表明，该方法能生成精度合理的碰撞概率（最高达10^{-3}），并可轻松集成到标准路径规划算法中，在包含不确定静态与动态障碍物的二维地图上规划出安全路径。更多资料、代码与视频见https://sites.google.com/view/ral-dcpf。
原文摘要 · Abstract (English)
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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