A Multi-UAV Path Planning Algorithm Based on DMPC-A* Fusion

Aiming at the problems of low efficiency and poor flight stability faced by path planning for multi-UAV cooperative target tracking and dynamic obstacle avoidance in complex three-dimensional airspace environments, A path optimization method based on the integration of distributed model predictive control (DMPC) and A* search algorithm was studied. The A* algorithm is utilized to generate the global initial paths of multiple unmanned aerial vehicles (UAVs), assign reasonable target trajectory points to each UAV, and provide basically feasible safe trajectories for the UAVs. The Bezier curve is integrated with the DMPC prediction model. By optimizing the parameters of the curve control points, the smoothness of the path and the continuity of the track are improved. Considering the dynamic constraints of the unmanned aerial vehicle, the track length constraints, the safety distance constraints and the communication condition constraints comprehensively, a multi-objective cost function is constructed and solved by rolling optimization to achieve real-time dynamic adjustment of the track. To balance multiple costs such as flight range, threat, energy consumption and control input, the cost weight coefficients are recalibrated to ensure the safety and global optimality of group flight. Meanwhile, in view of the problems of large computational load and poor real-time performance of the traditional centralized model predictive control (MPC), a distributed solution strategy is adopted, enabling each unmanned aerial vehicle to independently optimize the control input and achieve collaborative target tracking through information interaction, thereby significantly reducing the computational complexity of the algorithm. The experimental simulation environment adopts a three-dimensional space of 5.2 m×5.2 m×3.0 m, deploys 10 unmanned aerial vehicles and static obstacles with different shapes, and verifies the effectiveness of the method through multiple Python program simulation experiments. The results show that: Compared with the traditional algorithm, the DMPC-A* fusion method proposed in this paper can shorten the path length by approximately 4.2%. Besides, the track smoothness and stability are also improved. The algorithm proposed in this paper has good obstacle avoidance ability and environmental adaptability, providing technical support for the research on collaborative path planning for multiple unmanned aerial vehicles.