Evolving Collaborative Differential Evolution for Dynamic Multi-objective UAV Path Planning

The application of unmanned aerial vehicles (UAVs) in urban environments introduces complex challenges to path planning due to dynamic targets, changing environmental con ditions, and stringent safety requirements. To address these issues, this paper proposes an Evolving Collaborative Differential E...

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Veröffentlicht in:IEEE transactions on vehicular technology S. 1 - 13
Hauptverfasser: Xu, Renjie, Huang, Zhaoke, Wang, Chenwei, Yan, Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Adaptive Algorithms
Autonomous aerial vehicles
Collabora tive Learning
Costs
Differential Evolution (DE)
Dynamic Environments
Heuristic algorithms
Multi objective Optimization
Noise
Optimization
Path Plan-ning
Real-time systems
Safety
Trajectory
Unmanned Aerial Vehicles (UAVs)
Urban areas
Urban Navigation
Vehicle dynamics
ISSN:0018-9545, 1939-9359
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Zusammenfassung:The application of unmanned aerial vehicles (UAVs) in urban environments introduces complex challenges to path planning due to dynamic targets, changing environmental con ditions, and stringent safety requirements. To address these issues, this paper proposes an Evolving Collaborative Differential Evolution (ECDE) algorithm specifically designed for dynamic multi-objective UAV path planning. ECDE integrates an adaptive hyperparameter tuning mechanism, a robust change detection strategy, and a collaborative learning strategy to effectively balance multiple conflicting objectives, such as minimizing path length, energy consumption, noise pollution, and collision risk. Comprehensive experimental evaluations are conducted under various scenarios, including static environments, dynamic targets, and risk-adaptive urban navigation. The results demonstrate that ECDE consistently achieves superior global optimization performance and adaptability compared to state-of-the-art multi objective optimization methods, thereby significantly enhancing UAV operational effectiveness in complex, real-world urban scenarios.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3632847