Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

• Published in: Drones (Basel) Vol. 8; no. 2; p. 50
• Main Authors: Jiang, Yan, Bai, Tingting, Wang, Daobo, Wang, Yin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2024
• Subjects: Algorithms; Clustering; Collaboration; Constraint modelling; Control systems; coverage path planning; Decision making; Drone aircraft; Energy consumption; Fixed wings; fixed-wing UAV; Linear programming; Optimization; Path planning; pigeon-inspired optimization algorithm; Pigeons; Rotary wing aircraft; Trajectory optimization; Unmanned aerial vehicles; Unmanned helicopters; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8020050

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.