Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment
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Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment Lu Chang1 · Liang Shan1 · Chao Jiang2 · Yuewei Dai1,3 Received: 1 November 2019 / Accepted: 8 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Mobile robot path planning in an unknown environment is a fundamental and challenging problem in the field of robotics. Dynamic window approach (DWA) is an effective method of local path planning, however some of its evaluation functions are inadequate and the algorithm for choosing the weights of these functions is lacking, which makes it highly dependent on the global reference and prone to fail in an unknown environment. In this paper, an improved DWA based on Q-learning is proposed. First, the original evaluation functions are modified and extended by adding two new evaluation functions to enhance the performance of global navigation. Then, considering the balance of effectiveness and speed, we define the state space, action space and reward function of the adopted Q-learning algorithm for the robot motion planning. After that, the parameters of the proposed DWA are adaptively learned by Q-learning and a trained agent is obtained to adapt to the unknown environment. At last, by a series of comparative simulations, the proposed method shows higher navigation efficiency and successful rate in the complex unknown environment. The proposed method is also validated in experiments based on XQ-4 Pro robot to verify its navigation capability in both static and dynamic environment. Keywords Robot navigation · Path planning · DWA · Q-learning · Evaluation function
1 Introduction This work is supported by the Natural Science Foundation of Jiangsu Province (BK20191286) and the Fundamental Research Funds for the Central Universities (30920021139). Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10514-020-09947-4) contains supplementary material, which is available to authorized users.
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Liang Shan [email protected] Lu Chang [email protected] Chao Jiang [email protected] Yuewei Dai [email protected]
1
School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, People’s Republic of China
2
Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA
3
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, People’s Republic of China
In the fields of industrial and agricultural production, intelligent logistics, space exploration and emergency assistance, the application of mobile robots has become more widespread. Robots can carry different tools such as robotic arms, rangefinders, fire extinguishers to accomplish different tasks. The basis for completing the task is that the robot can move autonomously and adaptively. Path planning is one of the key technologies of mobile robots (Durrant 1994), which is described as finding a collision-
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