Transformer-based Heuristic for Advanced Air Mobility Planning

TL;DR

This paper presents a transformer-based neural network heuristic to improve risk-aware UAV path planning by reducing computational complexity and increasing efficiency in solving constrained shortest path problems.

Contribution

It introduces a novel transformer-based neural network heuristic and a new strategy that enhance the performance of risk-aware UAV path planning algorithms.

Findings

Reduced computational load in CSP problem solving

Improved path planning efficiency for UAVs

Enhanced safety constraints handling

Abstract

Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety of UAVs. This type of planning can be represented as a Constrained Shortest Path (CSP) problem, which seeks to find the shortest route that meets a predefined safety constraint. Solving CSP problems is NP-hard, presenting significant computational challenges. Although traditional methods can accurately solve CSP problems, they tend to be very slow. Previously, we introduced an additional safety dimension to the traditional A* algorithm, known as ASD A*, to effectively handle Constrained Shortest Path (CSP) problems. Then, we developed a custom learning-based heuristic using transformer-based neural networks, which significantly reduced computational load and enhanced the performance of the ASD A* algorithm. In this paper, we expand our dataset to include more risk maps and tasks, improve the proposed model, and increase its performance. We also introduce a new heuristic strategy and a novel neural network, which enhance the overall effectiveness of our approach.