Multi-Drone Optimal Mission Assignment and 3D Path Planning for Disaster Rescue

In a three-dimensional (3D) disaster rescue mission environment, multi-drone assignments and path planning are challenging. Aiming at this problem, assignment method based on adaptive genetic algorithms (AGA) using sine–cosine particle swarm optimization (SCPSO) proposed. First, an original 3D digital terrain model is constructed. Second, common threat sources in environments modeled, including mountains, transmission towers, severe weather. Third, cost–revenue function that considers factors such as drone performance, demand for points, elevation cost, sources, formulated to assign missions multiple drones. Fourth, AGA employed realize the assignment. To enhance convergence speed optimize performance finding optimal solution, both roulette elite retention Additionally, parameters of adjusted according changes fitness function. Furthermore, improved circle algorithm also used preprocess sequence AGA. Finally, model, SCPSO proposed flight between adjacent task points. addition, inertia acceleration coefficients linear weights designed so its escape local minimum, explore search space more thoroughly, achieve purpose global optimization. A multitude simulation experiments have demonstrated validity our method.