Trajectory planning, which determines a route from a starting position to a target position within a given airspace, is critical to unmanned aerial vehicle (UAV) mission execution. Many existing meta-heuristic approaches to three-dimensional (3D) trajectory planning aggregate competing requirements into a weighted cost and may suffer from limited adaptability when the environment changes. This paper formulates 3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and proposes a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA). The proposed method organizes objective-oriented swarms within a cooperative search framework and facilitates information exchange through archive sharing, thereby coordinating the search process among different objectives. The memory-time and diverse behavior strategies adjust search behaviors and solution perturbation to balance convergence and diversity. A hybrid change response strategy combines historical information reuse with diversity restoration after dynamic changes. Comparative experiments on dynamic benchmark problems and UAV trajectory planning scenarios demonstrate competitive convergence and adaptation performance, together with a favorable trade-off between solution quality and computational cost. Incremental ablation and parameter-sensitivity analyses further indicate the cumulative benefit of the integrated design and the stable performance of the selected parameter configuration across the tested settings.
Gengsong Li, Yi Liu, Qibin Zheng et al.· Applied Sciences· 0 citations
Dynamic events require coordinated task allocation (TA) and path planning (PP) for multiple unmanned aerial vehicles (UAVs) to maintain executable mission progress. Existing coupled methods often use path information only as a precomputed cost or downstream refinement result, limiting its reuse after dynamic changes. This paper formulates dynamic task allocation and path planning (DTAPP) as a dynamic multi-objective optimization problem considering remaining target value, mission makespan, path feasibility, and execution-state inheritance. A three-role dynamic multi-swarm crow search algorithm (3R-DMCSA) is proposed, in which exploiter, explorer, and diversifier role-based swarms share a crow search-based update structure, feasibility-aware comparison, and leader-selection structure but use TA- and PP-specific encodings, objective preferences, initialization, and dynamic responses. A path matrix connects the layers by storing candidate paths and their attributes, which are fed back to TA, and supporting rolling-horizon leading flight-segment refinement. Experiments involving three dynamic urban scenarios compare the method with five baselines and evaluate its path-matrix feedback and rolling-horizon refinement. Compared with the strongest baseline, our approach improves mission-value acquisition by 10.6%, 16.2%, and 32.0% in the three scenarios, while maintaining near-complete target coverage and reliable flight-segment execution. Path-matrix feedback improves mission-value acquisition by 5.2–26.1% over the configuration without PP-to-TA path feedback, while rolling-horizon segment refinement reduces replanning latency by 48.8–70.5% compared with refining all planned segments without significantly compromising mission performance.
Gengsong Li, Yi Liu, Qibin Zheng et al.· Drones· 0 citations