Multi-UAVs cooperative task allocation and path planning for low-altitude logistics

TeCoR-UAV: A Two-Stage Topology Extraction and Cooperative Routing Algorithm for Low-Altitude Logistics

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application.

Multi-Objective Trajectory Planning Method for Air–Ground Collaborative Logistics UAVs Under Preemptive Scheduling

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays.

Path-Matrix-Coupled Dynamic Task Allocation and Path Planning for Multi-UAV Systems

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.

A Multi-UAV Planning Framework for Task Allocation, Route Optimization and Trajectory Smoothing

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.

Centralized Scheduling and Distributed Trajectory Optimization for Cooperative Mapping with Multiple PX4-Based VTOL UAVs

Dynamic Multiobjective Co-Optimization of Unmanned Aerial Vehicle Placement, Task Scheduling, and Resource Allocation for Remote Sensing

This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the total energy consumption of UAVs. Addressing the shortcomings of existing research, which often overlooks the computational capabilities of UAVs, optimizes only a single aspect, and fails to account for environmental dynamics, this work formulates the problem as a dynamic multiobjective optimization problem. A hybrid optimization framework named DSG, integrating swarm intelligence and evolutionary algorithms, is proposed. The framework first derives a closed-form optimal resource allocation solution for given deployment and scheduling strategies through theoretical analysis. It then employs an improved dynamic multiobjective evolutionary algorithm (DMOEA) to co-optimize UAV positions (continuous variables) and task scheduling (discrete variables). Experimental results demonstrate that DSG achieves significantly better normalized hypervolume performance than comparative algorithms across various system scales [number of UAVs, access points (APs), and sensors] while exhibiting good stability and scalability. This provides an effective solution for the efficient co-optimization of UAV-assisted edge computing in dynamic environments.