Path planning and motion control are core technologies for autonomous navigation of unmanned vehicles (UAVs). Addressing the problems of poor adaptability and reliance on precise models in dynamic environments

traditional methods propose a reinforcement learning-based path planning and motion control method for UAVs

focusing on solving key technical issues in practical deployment from an application perspective. First

a collaborative architecture integrating A* global planning and reinforcement learning local control is constructed. The A* algorithm generates the global guidance path

while a deep reinforcement learning agent is responsible for dynamic obstacle avoidance and trajectory tracking. Based on this

the engineering implementation details

including state space design

reward function construction

and network structure configuration

are systematically described. Experiments in three typical scenarios are conducted in the Gazebo simulation environment. The results show that in static obstacle environments

the proposed method achieves a navigation success rate of 96%

with a path length reduction of 6.2% compared to traditional methods; in dynamic obstacle environments

the success rate remains above 92%

with an average collision distance increase of 17.4%; and in moving target point scenarios

task completion time is reduced by 15.3%. Finally

key issues and solutions in practical applications

such as Sim2Real transfer and multi-machine collaboration

are discussed.