Abstract:

Aiming at the problem of low convergence efficiency and high algorithmic complexity of the path planning model of the Unmanned Aerial Vehicle(UAV) due to multiple factors such as wind conditions and obstacles in a multi-constraint unknown environment,we propose a path planning strategy based on progressive reinforcement learning(Progressive Deep Reinforcement Q-learning Network,PR-DQN).The algorithm considers the class-teaching training and learning method,and by constructing feature-differentiated scenarios and dynamically adjusting the UAV training scenarios during the model training process,it solves the learning difficulties caused by the model facing the complex task too early,avoids the model falling into the local optimum,and improves the model learning efficiency.In addition,the algorithm comprehensively considers the impact of multiple constraints such as wind conditions,obstacles and energy consumption on the flight trajectory of the UAV in the unknown environment,and constrains the path selection of the UAV in flight by constructing the energy consumption,collision factor and multi-constraint reward function,which ensures that the UAV completes the path planning task as long as the safety and energy consumption are allowed.Experimental results show that the average planning success rate of the scheme proposed in the paper is approximately 5.4% higher than that of similar algorithms,and the average training overhead is lower than that of similar algorithms by approximately 11.7%,which makes the PR-DQN algorithm highly promising for application in an unknown environment where multiple types,multiple numbers of obstacles,and multivariate energy consumption coexist.

Key words: unmanned aerial vehicle, path planning, reinforcement learning, asymptotic, multi-constraint reward function