用神经网络加速安全避障计算，实现实时高精度避障。
NeuroHJR: Hamilton-Jacobi Reachability-based Obstacle Avoidance in Complex Environments with Physics-Informed Neural Networks
- 用物理信息神经网络逼近哈密顿-雅可比可达集，避免网格离散化
- 在密集障碍场景中达到与传统方法相当的安全性，计算成本大幅降低
- 适合需要实时安全避障的机器人系统部署
自主地面车辆（AGVs）需在复杂环境中安全导航，同时应对动态系统和环境不确定性。哈密顿-雅可比可达性（HJR）通过前向与后向可达集计算提供形式化安全保证，但其在多障碍环境中的应用受限于较差的可扩展性。本文提出新型框架NeuroHJR，利用物理信息神经网络（PINNs）近似HJR解，实现实时避障。通过将系统动力学与安全约束嵌入神经网络损失函数，该方法摆脱了网格离散化依赖，可在连续状态空间中高效估计可达集。仿真结果表明，在密集障碍场景中，该方法达到与经典HJR求解器相当的安全性能，同时显著降低计算开销。本工作为机器人领域中基于可达性的实时、可扩展避障部署提供了新路径。
原文摘要 · Abstract (English)
Autonomous ground vehicles (AGVs) must navigate safely in cluttered environments while accounting for complex dynamics and environmental uncertainty. Hamilton-Jacobi Reachability (HJR) offers formal safety guarantees through the computation of forward and backward reachable sets, but its application is hindered by poor scalability in environments with numerous obstacles. In this paper, we present a novel framework called NeuroHJR that leverages Physics-Informed Neural Networks (PINNs) to approximate the HJR solution for real-time obstacle avoidance. By embedding system dynamics and safety constraints directly into the neural network loss function, our method bypasses the need for grid-based discretization and enables efficient estimation of reachable sets in continuous state spaces. We demonstrate the effectiveness of our approach through simulation results in densely cluttered scenarios, showing that it achieves safety performance comparable to that of classical HJR solvers while significantly reducing the computational cost. This work provides a new step toward real-time, scalable deployment of reachability-based obstacle avoidance in robotics.
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