A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment

被引：7

作者：

Hao, Bing ^{[1
]}

Du, He ^{[1
]}

Zhao, Jianshuo ^{[1
]}

Zhang, Jiamin ^{[1
]}

Wang, Qi ^{[1
]}

机构：

[1] Qiqihar Univ, Coll Comp & Control Engn, Qiqihar, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | 2022年 / 2022卷

关键词：

NAVIGATION; STRATEGIES; ALGORITHM;

D O I：

10.1155/2022/2540546

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：12

共 35 条

[1] Mona: an Affordable Open-Source Mobile Robot for Education and Research [J].

Arvin, Farshad ;

Espinosa, Jose ;

Bird, Benjamin ;

West, Andrew ;

Watson, Simon ;

Lennox, Barry .

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 2019, 94 (3-4) :761-775

[2] Multi-Robot Path Planning Method Using Reinforcement Learning [J].

Bae, Hyansu ;

Kim, Gidong ;

Kim, Jonguk ;

Qian, Dianwei ;

Lee, Sukgyu .

APPLIED SCIENCES-BASEL, 2019, 9 (15)

[3]

Caruana R., 2006, Proceedings of the 23 rd International Conference on Machine Learning, V148, P161, DOI 10.1145/1143844.1143865

[4] An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge [J].

Chen, Chen ;

Jiang, Jiange ;

Lv, Ning ;

Li, Siyu .

IEEE ACCESS, 2020, 8 :99059-99069

[5]

Dijkstra EW., 1959, NUMER MATH, V1, P269, DOI [10.1007/BF01386390, DOI 10.1007/BF01386390]

[6]

Dorigo M., 1997, IEEE Transactions on Evolutionary Computation, V1, P53, DOI 10.1109/4235.585892

[7]

Eberhart R., 1995, P INT S MICR HUM SCI, P39, DOI [DOI 10.1109/MHS.1995.494215, 10.1109/MHS.1995.494215]

[8] An extensive review of computational intelligence-based optimization algorithms: trends and applications [J].

Goel, Lavika .

SOFT COMPUTING, 2020, 24 (21) :16519-16549

[9]

Haarnoja T, 2018, PR MACH LEARN RES, V80

[10] A FORMAL BASIS FOR HEURISTIC DETERMINATION OF MINIMUM COST PATHS [J].

HART, PE ;

NILSSON, NJ ;

RAPHAEL, B .

IEEE TRANSACTIONS ON SYSTEMS SCIENCE AND CYBERNETICS, 1968, SSC4 (02) :100-+

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