リスクを考慮した経路計画のための学習加速A*探索
Learning-accelerated A* Search for Risk-aware Path Planning
安全制約付き最短経路問題を解くため、A*に安全次元を追加し、Transformerベースの学習ヒューリスティックで計算を高速化した手法を提案。
著者: Jun Xiang, Junfei Xie, Jun Chen
分類: cs.RO
原文アブストラクト
Safety is a critical concern for urban flights of autonomous Unmanned Aerial Vehicles. In populated environments, risk should be accounted for to produce an effective and safe path, known as risk-aware path planning. Risk-aware path planning can be modeled as a Constrained Shortest Path (CSP) problem, aiming to identify the shortest possible route that adheres to specified safety thresholds. CSP is NP-hard and poses significant computational challenges. Although many traditional methods can solve it accurately, all of them are very slow. Our method introduces an additional safety dimension to the traditional A* (called ASD A*), enabling A* to handle CSP. Furthermore, we develop a custom learning-based heuristic using transformer-based neural networks, which significantly reduces the computational load and improves the performance of the ASD A* algorithm. The proposed method is well-validated with both random and realistic simulation scenarios.
