NeuroHJR: 物理情報ニューラルネットワークによる複雑環境でのハミルトン・ヤコビ到達可能性に基づく障害物回避
NeuroHJR: Hamilton-Jacobi Reachability-based Obstacle Avoidance in Complex Environments with Physics-Informed Neural Networks
物理情報ニューラルネットワークを用いてハミルトン・ヤコビ到達可能性の解を近似し、多数の障害物がある環境でもリアルタイムな障害物回避を可能にするフレームワークを提案した。
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著者: Granthik Halder, Rudrashis Majumder, Rakshith M R, Rahi Shah, Suresh Sundaram
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
