运动规划
路径(计算)
计算机科学
人工神经网络
算法
趋同(经济学)
数学优化
局部最优
领域(数学)
人工智能
数学
机器人
经济增长
经济
程序设计语言
纯数学
作者
Fuchen Kong,Qi Wang,Shang Gao,Hualong Yu
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 44051-44064
被引量:3
标识
DOI:10.1109/access.2023.3273164
摘要
Deep Q-network (DQN) is one of the standard methods to solve the Unmanned Aerial Vehicle (UAV) path planning problem. However, the way agent deepens its cognition of the environment through frequent random trial-and-error leads to slow convergence. This paper proposes an optimized DQN with Artificial Potential Field (APF) as prior knowledge called B-APFDQN for path planning. Replacing the traditional neural network which has only one Q-value output with a multi-output neural network to promote the training process in combination with APF. Furthermore, a SA- $\varepsilon $ -greedy algorithm that can automatically adjust the stochastic exploration frequency with steps and successes is proposed in order to prevent the agent from falling into local optimum. We remove the nodes that do not affect the path connectivity and apply the B-spline algorithm to make the path shorter and smoother. Simulation experiments show that the proposed B-APFDQN algorithm performs better than the classical DQN, has a strong ability to avoid falling into local optimum, and the obtained paths are smooth and shorter, which proves the effectiveness of B-APFDQN in the UAV path planning problem.
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