A Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing
TL;DR
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 planning.
Abstract
Coordinated mission planning for multiple unmanned aerial vehicles in cluttered static three-dimensional environments requires consistent treatment of obstacle-aware motion, fleet-level task allocation, route sequencing, and executable trajectory generation. In many existing approaches, these elements are optimized separately, or fleet-level decisions are made using simplified geometric distances that do not accurately reflect UAV-specific motion feasibility in obstacle-constrained space. This paper presents a Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing for static environments with known obstacle geometry. In the first stage, an offline single-UAV planner based on a hybrid Differential Evolution and Enhanced Whale Optimization Algorithm computes feasible raw paths for all relevant ordered node pairs and constructs a UAV-specific directed travel-cost matrix. In the second stage, these planner-derived matrices are used for feasibility-aware balanced task distribution and route optimization with exchange-based refinement under a composite total-cost–makespan objective. In the third stage, the raw paths corresponding to the final selected routes are reconstructed and transformed into executable trajectories by adaptive cubic B-spline smoothing. Experimental evaluation was conducted at the local-planning, fleet-planning, and smoothing levels in known static environments. The hybrid planner generated high-quality pairwise obstacle-avoiding paths and exhibited favorable convergence behavior relative to standard WOA, PSO, DE, SOS, and GWO in the tested scenarios. At the fleet level, the full framework reduced makespan by 2.1–3.4% and the composite objective by 0.9–1.4% relative to balanced partitioning without exchange refinement on benchmark instances. In the smoothing stage, the adaptive cubic B-spline reduced path length by 8.5% and maximum curvature by 41.5% relative to the unsmoothed polyline representation. These results demonstrate that the proposed hierarchical formulation is computationally effective, physically consistent, and well suited to multi-UAV mission planning.