Synchronous Task Allocation and Trajectory Optimization for Autonomous Drone Swarm

As applications become increasingly complex, robust solutions are needed for coordinating drone swarms to perform tasks autonomously. Efficient task assignment and trajectory planning are essential for optimizing the coordination and performance of swarm flying robots across various scenarios. This paper proposes a strategy to simultaneously address allocation and path planning problems for swarm flying robots, with the aim of optimally and efficiently assigning tasks and planning paths for multiple drones. Leveraging coordination and local interactions among drones, the algorithm optimally assigns tasks to individual drones and generates collision-free trajectories for each member. Through extensive simulation evaluations, this work demonstrates the effectiveness of the proposed algorithm in achieving optimal trajectories while ensuring dynamic feasibility and inter-drone collision avoidance. The results highlight the algorithm’s potential to enhance the coordination and performance of swarm flying robots in various real-world applications, providing a versatile solution to address task allocation and path planning challenges.