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Published on: October 14, 2017
A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment
Bing Hao1, He Du1, Jianshuo Zhao1
1College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
Abstract:
The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field and dynamic reward function to generate a feasible path. The proposed algorithm has a significant improvement in computing time and convergence speed compared to its classical counterpart. Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect to two metrics: path length and turning angle. The simulation results show the effectiveness and practicality of the proposal for mobile robot path planning.
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