Learning-accelerated A* Search for Risk-aware Path Planning
<|reference_start|>Learning-accelerated A* Search for Risk-aware Path Planning: 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.<|reference_end|>
@article{xiang2024learning-accelerated,
    title={Learning-accelerated A* Search for Risk-aware Path Planning},
    author={Jun Xiang, Junfei Xie and Jun Chen},
    journal={AIAA SCITECH 2024 Forum, p.2895, (2024)},
    year={2024},
    doi={10.2514/6.2024-2895},
    archivePrefix={arXiv},
    eprint={2409.11634},
    primaryClass={cs.RO}
}