Learning-accelerated A* Search for Risk-aware Path Planning

TL;DR

This paper introduces a learning-accelerated A* algorithm for risk-aware path planning in urban UAV flights, combining a safety-aware extension of A* with a transformer-based heuristic to efficiently solve the NP-hard Constrained Shortest Path problem.

Contribution

It presents a novel ASD A* algorithm that incorporates safety constraints and a transformer-based heuristic to significantly speed up risk-aware path planning.

Findings

The method effectively reduces computation time in simulations.

It successfully finds safe, efficient paths in complex environments.

The approach outperforms traditional CSP solvers in speed.

Abstract

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.