This paper presents a learning-augmented deterministic method for heterogeneous multi-UAV task allocation and routing in static, fully observed environments. UAV-specific asymmetric path-length matrices represent the output of an offline path planner, allowing online planning to focus on assignment and route construction. The deterministic core evaluates insertions, checks energy feasibility, prioritizes tasks with few feasible UAVs, repairs blocked states, and improves completed routes. A shallow multilayer perceptron ranks feasible candidates by predicting future regret, defined as the difference between the makespan after deterministic completion and the immediate post-insertion makespan. The objective minimizes makespan first and uses aggregate route length only when makespan values are equal within the prescribed numerical tolerance. The model was trained on 12 synthetic instances and evaluated on 20 separate instances, each containing 20 tasks and four heterogeneous UAVs under uniform, clustered, and mixed layouts. It was compared with two greedy insertion heuristics, the corresponding deterministic ablation (CT-D), and a linear-regret variant. The proposed method achieved a mean makespan of 106.24 versus 107.46 for CT-D, a descriptive reduction of approximately 1.1%. Compared with CT-D, aggregate route length increased by approximately 1.5%, while mean runtime increased from 0.059 to 0.233 s. Performance improved on clustered and mixed layouts but declined slightly on uniform layouts.

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.

Safe and energy-aware navigation for unmanned aerial vehicles (UAVs) requires the simultaneous optimization of path length, curvature, obstacle clearance, altitude, energy expenditure, and mission time—within the tight computational limits of on-board processors. This study proposes a two-phase hybrid optimizer that couples the global search capability of Differential Evolution (DE) with an Enhanced Whale Optimization Algorithm (E-WOA) specialized for local refinement. E-WOA improves on the canonical WOA through three principled modifications: real-time boundary repair to ensure path feasibility, quasi-oppositional learning to restore population diversity, and an adaptive stagnation trigger that re-initiates exploration when progress stalls. When the population’s improvement plateaus, control transfers from DE to E-WOA, combining broad exploration with focused exploitation. Comparative experiments conducted in 3D environments with static obstacles that block direct line-of-sight routes demonstrate that the hybrid achieves lower composite cost—normalized over path length, curvature, risk, altitude, energy and time—shorter and smoother trajectories, and faster convergence than standard metaheuristics while preserving obstacle clearances and curvature limits. Averaged over 30 independent trials, our hybrid framework reduced the normalized composite cost by 14.5% relative to the next-best algorithm (Grey Wolf Optimizer) and produced feasible paths in an average of 2.35 seconds on commodity hardware—adequate for strategic re-planning, though further optimization is needed for sub-second control loops. Blending DE’s global reach with a diversity-aware, adaptively stalled WOA provides a practical foundation for strategic, near-real-time replanning in 3D airspaces.

This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents.