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Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

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Abstract

Path planning in dynamic environment is a great challenge for mobile robot. A large number of approaches have been used to deal with it. Since the neural network algorithm has the ability to find the optimal solution at high speed and self-learning function, it has achieved extensive applications in the path planning tasks. Considering that the optimization performance of the neural network heavily depends on the quality of the training sample, this paper proposes a novel way to provide the training samples for the neural network. Work space of the robot is divided into two parts: global safe area and local dangerous area. In the global safe area, the robot only receives the attraction force from the target and it moves towards the target directly. In the dangerous area, except the attraction force, the robot also receives the repulsion force from the obstacle(s). The repulsion force and the angle between the obstacle and the target (origin of the coordinate is in the position of the robot) are used to be the inputs of the fuzzy inferencing system, and the deflection angle of the robot is the output. The final moving direction of the robot is determined by summing this deflection angle and the direction of the attraction force. The coordinates of the target and obstacle, and the moving direction of the robot corresponding to this position relationship, constitute the training samples for the neural network. Benefited from the precise moving direction obtained by the fuzzy artificial potential field algorithm, the neural network gets excellent path optimization ability. Simulation and physical experiment results demonstrate the potential of the proposed algorithm.

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References

  1. Adamu PI, Okagbue HI, Oguntunde PE (2019) Fast and optimal path planning algorithm (faoppa) for a mobile robot. Wirel Pers Commun 106:577–592

    Article  Google Scholar 

  2. Hong YD, Kim IS, Lee WK (2018) Simple global path planning algorithm using a ray-casting and tracking method. J Intell Robot Syst 90:101–111

    Article  Google Scholar 

  3. Li C, Fang C, Wang F (2019) Complete coverage path planning for an arnold system based mobile robot to perform specific types of missions. Front Inf Technol Electron Eng 20:1530–1542

    Article  Google Scholar 

  4. Rahman MM, Ishii K, Noguchi N (2019) Optimum harvesting area of convex and concave polygon field for path planning of robot combine harvester. Intel Serv Robot 12:167–179

    Article  Google Scholar 

  5. Saha O, Dasgupta P, Woosley B (2019) Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options. Auton Robots 43:2071–2093

    Article  Google Scholar 

  6. Huang H, Huang P, Zhong S, Long T, Wang S, Qiang E, Zhong Y (2019) Dynamic path planning based on improved d* algorithms of gaode map. In: Proceedings of the 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pages 1121–1124, Chengdu, China, March 15-17

  7. Dugarjav B, Lee SG, Kim D, Kim JH, Chong NY (2013) Scan matching online cell decomposition for coverage path planning in an unknown environment. Int J Precis Eng Manuf 14:1551–1558

    Article  Google Scholar 

  8. Fu M, Wang S, Wang Y (2019) Multi-behavior fusion based potential field method for path planning of unmanned surface vessel. China Ocean Eng 33:583–592

    Article  Google Scholar 

  9. Qian Q, Wu J, Wang Z (2019) Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm. Pers Ubiquit Comput 23:393–403

    Article  Google Scholar 

  10. Goher KM, Fadlallah SO (2019) Control of a two-wheeled machine with two-directions handling mechanism using pid and pd-flc algorithms. Int J Autom Comput 16:511–533

    Article  Google Scholar 

  11. Hwu T, Wang AY, Oros N, Krichmar JL (2018) Adaptive robot path planning using a spiking neuron algorithm with axonal delays. IEEE Trans Cognit Dev Syst 10(2):126–137

    Article  Google Scholar 

  12. Wang D, Duan Y, Weng J (2018) Motivated optimal developmental learning for sequential tasks without using rigid time-discounts. IEEE Trans Neural Netw Learn Syst 29(10):4917–4931

    Article  Google Scholar 

  13. Wang D, Si W, Luo Y, Wang H, Ma T (2019) Goal-directed autonomous navigation of mobile robot based on the principle of neuromodulation. Netw Comput Neural Syst 30(1–4):79–106

    Article  Google Scholar 

  14. Guo J, Li C, Guo S (2019) A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control. IEEE Access 8:1394–1405

    Article  Google Scholar 

  15. Ivanov V (2015) A review of fuzzy methods in automotive engineering applications. Eur Transp Res Rev 7:1–10

    Article  Google Scholar 

  16. Mbede JB, Huang X, Wang M (2000) Fuzzy motion planning among dynamic obstacles using artificial potential fields for robot manipulators. Robot Auton Syst 32(1):61–72

    Article  Google Scholar 

  17. Parhi DR, Mohanta JC (2011) Navigational control of several mobile robotic agents using petri-potential-fuzzy hybrid controller. Appl Soft Comput 11:3546–3557

    Article  Google Scholar 

  18. Phan D, Yang J, Grosu R, Smolka SA, Stoller SD (2017) Collision avoidance for mobile robots with limited sensing and limited information about moving obstacles. Form Methods Syst Des 51:62–86

    Article  Google Scholar 

  19. Gao W, Tang Q, Ye B, Yang Y, Yao J (2020) An enhanced heuristic ant colony optimization for mobile robot path planning. Soft Comput 24:6139–6150

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the Scientific Problem Tackling of Henan Province (192102210256). The authors also thank the help from National Supercomputing Center in Zhengzhou.

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Authors and Affiliations

  1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, People’s Republic of China

    Dongshu Wang & Shuli Chen

  2. The People’s Bank of China, Zhengzhou Central Sub-Branch, Zhengzhou, 450018, People’s Republic of China

    Lei Liu

  3. Institute of Public safety, Zhengzhou University, Zhengzhou, 450001, People’s Republic of China

    Dongshu Wang

  4. China Railway Engineering Equipment Group Co., Ltd., Zhengzhou, 450016, People’s Republic of China

    Yadong Zhang

Authors
  1. Dongshu Wang
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  2. Shuli Chen
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  3. Yadong Zhang
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  4. Lei Liu
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Corresponding author

Correspondence to Yadong Zhang.

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Wang, D., Chen, S., Zhang, Y. et al. Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network. Artif Life Robotics 26, 129–139 (2021). https://doi.org/10.1007/s10015-020-00630-6

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  • DOI: https://doi.org/10.1007/s10015-020-00630-6

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