Abstract:

Flying vehicles have attracted significant attention in urban traffic， rescue transportation， and other operational fields. Efficient multi-modal task path planning effectively improves their operational efficiency in these fields. Therefore， an efficient learning method for multi-modal task path planning of flying vehicles is proposed. Firstly， the action space of the flying vehicle is optimized， retaining the actions of take-off， landing， and moving towards the target position. Simultaneously， a probability selection mechanism for non-target direction actions is designed. Secondly， considering the air-ground coordination characteristics of the flying vehicle， a novel reward function of Q-learning is designed. And a reward enhancement mechanism based on historical optimal path experience is proposed. Finally， a path smoothing method is proposed to obtain a smooth and continuous path for the air-ground cooperative task. Compared with the multi-modal paths planned by A*， Q-learning， and D* Lite， the multi-modal path planned by this method successively reduces the running distance by 10.35， 126.75， and 162.10 m， respectively. In terms of learning efficiency， the method reduces the learning time by 45.97% compared to Q-learning.

Key words: flying vehicles, multi-modal task path planning, action space, reward function, path smoothing