Path Planning and Obstacle Avoidance of Formation Flight

Abstract

This study applies path planning and obstacle avoidance to drone control for conducting riverbank inspections. Given that the river's surrounding environments are often windy and filled with overgrown branches and unknown obstacles, this study improves path planning and obstacle avoidance to enable drones to complete inspection tasks using the planned optimal route. Multiple drones are used for larger-scale areas to reduce time consumption and increase efficiency. Regarding path planning, the A* algorithm is improved using a grid-based approach. For obstacle avoidance, depth cameras are installed on the drones, and the obtained images are processed by reinforcement Q-learning with a genetic algorithm to navigate around obstacles. Since maintaining formation is necessary during task execution, the leader-follower method of formation flight ensures that multiple drones can complete inspection tasks while maintaining formation.