Archive/A Hybrid A*–APF Path Planning Framework with Payload Stability Constraints for Cargo UAVs in Continuous Heterogeneous Environments
A Hybrid A*–APF Path Planning Framework with Payload Stability Constraints for Cargo UAVs in Continuous Heterogeneous Environments
Yong Wang, Dayuan Zhang, Xi Vincent Wang et al.
14 de julio de 2026
en

Abstract

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning framework that embeds trajectory smoothness optimization directly into the planning process rather than treating it as a post-processing step. An improved A* algorithm is developed by incorporating a trajectory smoothness term into its cost function to penalize sharp turns during global path generation. The resulting path is further refined using an enhanced artificial potential field (APF) method with virtual target points and multi-field force synthesis to mitigate local minima. In addition, the Ramer–Douglas–Peucker algorithm is employed to remove redundant waypoints, and a trajectory generation module based on B-spline interpolation and minimum snap optimization is introduced to produce smooth and dynamically feasible trajectories. Numerical simulation results demonstrate that, in indoor warehouse environments, the proposed method reduces the average turning angle by 88.4% (to 23.1°) compared with the standard A* algorithm while maintaining a comparable path length of 135.11 m. In large-scale outdoor urban scenarios, it achieves a path smoothness of 0.0124 with an average turning angle of 40.0°, substantially outperforming the Genetic Algorithm (104.6°) and Particle Swarm Optimization (83.5°) on turning angle while delivering competitive computation times of 0.52–1.51 s. An ablation study confirms that the improved A* and enhanced APF components each contribute independently to turning angle reduction and local minima avoidance, respectively, and that their integration yields the optimal balance across all metrics. These results indicate the proposed framework’s effectiveness for UAV-based last-mile delivery in scenarios requiring seamless indoor–outdoor transitions under payload stability constraints.

IPC Classification

G06B60

Keywords

hybridpathplanningframeworkpayloadstabilityconstraintscargouavscontinuousheterogeneousenvironmentsdronesunmannedaerialvehiclesindooroutdoorposescriticalchallengepromotingsharpturns
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