Research on Indoor UAV 3D Path Planning Based on Optimal Ant Colony-Artificial Potential Field Method

Based on the path planning algorithm, the optimal path planning from the starting point to the endpoint and the dynamic obstacle avoidance during the flight in an indoor environment of the unmanned aerial vehicle (UAV) is studied. A fusion algorithm that combines the optimized ant colony algorithm and the optimized artificial potential field method is used. In this fusion algorithm, the angle and feasibility factors are added to the heuristic function of the ant colony algorithm to diversify the heuristic function, changes the pheromone update method and adds a fallback mechanism of the ant colony algorithm, and improves the repulsive potential field function and gravitational function of the artificial potential field method, respectively. The ant colony algorithm performs the global path planning, and the artificial potential field method performs the local path planning. The simulation results show that the optimized fusion algorithm convergence speed is faster, the planned path is smoother and the improved algorithm has dynamic obstacle avoidance capability compared with the traditional ant colony algorithm. The virtual space simulation in the ROS system shows that the algorithm has certain practicality and feasibility.