Distributed Task Allocation and Trajectory Planning for Heterogeneous UAV Swarms in Multi-Constraint Environments

E-CBBA: A Consensus Auction Approach for Multi-UAV Task Allocation Under Time Window and Task Priority Constraints

This paper proposes a collaborative task allocation model for heterogeneous multi-UAV systems in missions with chain-structured task priorities and strict time constraints. The model considers key constraints, including UAV flight speed, task capability differences, execution limits, and ordered task dependencies, enabling accurate representation of coordinated chain task execution. Traditional approaches struggle to ensure both real time performance and feasibility when the number of tasks and UAVs grows. To address this challenge, we develop an Extended Consensus-Based Bundle Algorithm (E-CBBA). The algorithm extends the traditional CBBA framework by introducing a maximum gain task screening strategy, a feasible position set for path insertion, and a global task execution time matrix for distributed consensus and conflict resolution. These mechanisms ensure that each UAV evaluates and inserts tasks only under feasible priority, timing, and kinematic conditions. We prove the convergence and approximate optimality of E-CBBA. Extensive simulations show that E-CBBA achieves higher objective values and task completion rates than traditional CBBA and GA-based methods, while maintaining fast convergence, scalability to multiple sequential task types, robustness under moderate communication degradation, and feasibility in ROS 2 and Gazebo simulations. Note to Practitioners—Multiple unmanned aerial vehicles (UAVs) are increasingly deployed in real combat and emergency response missions, where tasks are often subject to strict time constraints and precedence relationships. As the number of tasks and UAVs grows, existing task allocation methods tend to suffer from excessive decision latency and frequent task conflicts, which severely degrade coordination efficiency. To address these challenges, we propose an extended consensus-based bundle algorithm (E-CBBA) that enables UAVs to rapidly identify feasible tasks and achieve coordinated task allocations while explicitly respecting task ordering and temporal constraints. The proposed algorithm extends the conventional CBBA framework by incorporating task dependency modeling and time feasibility checking, allowing scalable and conflict-free coordination in complex mission scenarios. Simulations demonstrate that the method significantly improves task completion rates and decision speed, and remains effective as the number of tasks and UAVs increases. Future work will incorporate online learning to enhance robustness. The algorithm can also be extended to multi-robot coordination scenarios such as emergency rescue, logistics delivery, and inspection missions.

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

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.

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

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.

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

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

To address the challenges of collaborative task allocation and path planning for multiple logistics unmanned aerial vehicles (UAVs) in urban low-altitude environments, this paper proposes a bilevel nested joint optimization method based on reinforcement learning and a graph search algorithm to enhance the efficiency of collaborative last-mile delivery by multiple logistics UAVs while reducing flight risks. The proposed method constructs a bilevel architecture system based on a task allocation and decision-making model and a path planning model. The upper-level model holistically considers the demands of three stakeholders—government (safety), customers (timeliness), and UAV enterprises (economy)—at the macro level. Then, based on real-time order information and UAV status, a multi-objective optimization and constraint model is constructed under complex dynamic environments. A multi-agent proximal policy optimization algorithm is employed to achieve rapid dynamic task allocation and decision-making. The lower-layer model utilizes the upper-level allocation results combined with detailed environmental information to plan safe and efficient flight paths for each UAV at the micro level. It employs an improved jumping-point search algorithm for refined path optimization. A loop feedback mechanism is designed to facilitate information exchange between layers, thereby coupling the task allocation and path planning processes to achieve collaborative optimization of upper- and lower-level task allocation and decision-making. This method effectively addresses complex logistics delivery scenarios, enhancing the overall efficiency and robustness of the delivery system. Simulation experiments comprehensively consider path influences from flexible open-area delivery, varying numbers of distribution centers and UAVs, and on-demand rush orders. Tests conducted in medium- and high-density environments demonstrate the proposed model and algorithm’s significant superiority in dynamic complex scenarios. Even when confronted with complex environments and dynamic order scenarios, it consistently generates highly applicable UAV flight paths.

Heterogeneous Networked Multi-UAV Cooperative Task Allocation Based on a Distributed Performance Impact Algorithm

A dual-fallback mechanism is constructed by a pruning-redundancy coalition formation scheme and a task- segmentation scheme, thereby enhancing both system operational efficiency and task-completion certainty of the proposed DPI algorithm.