TL;DR
Simulation results show that the proposed hierarchical task planning framework significantly outperforms traditional approaches in efficiency, robustness, and scalability, highlighting its strong potential for UAV swarm mission planning in complex environments.
Experimental results show that the proposed improved dung beetle optimization (IDBO) algorithm, which integrates multiple coordinated mechanisms to enhance the original dung beetle optimizer, can serve as a competitive optimizer for numerical benchmark problems and offline static 3D UAV path-planning simulations.
3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA) are formed.
The Circle-SPM chaotic map is introduced to optimize the population initialization process, effectively mitigating the premature convergence caused by uneven distribution and a lack of population diversity.
A multi-objective intelligent optimization algorithm, the wise wayfinding algorithm (WWA), which integrates mechanisms from non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (MOPSO) and exhibits favorable convergence and robust solution distribution on standard benchmark functio...
Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP.
A Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing for static environments with known obstacle geometry is presented and results demonstrate that the proposed hierarchical formulation is computationally effective, physically consistent, and well suited to multi-UAV mission p...
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