A Bayesian Neural Network-based Obstacle Avoidance Algorithm for an Educational Autonomous Mobile Robot Platform

Authors

  • Anh Hoang Hanoi University of Science and Technology, Vietnam
  • Son Thanh Nguyen Hanoi University of Science and Technology, Vietnam
  • Tuan Van Pham Vinh University of Technology Education, Vietnam
  • Tu Minh Pham Hanoi University of Science and Technology, Vietnam
  • Linh Viet Trieu Hanoi University of Science and Technology, Vietnam
  • Trung Thanh Cao Hanoi University of Science and Technology, Vietnam
Volume: 13 | Issue: 6 | Pages: 12183-12189 | December 2023 | https://doi.org/10.48084/etasr.6304

Received: 22 August 2023 | Revised: 21 September 2023 | Accepted: 29 September 2023 | Online: 5 December 2023


Corresponding author: Son Thanh Nguyen

Abstract

Autonomous mobile robots belong to automatically controlled objects that are designed and produced for various demands. This study aimed to develop an inexpensive platform of autonomous mobile robots that can be used for educational and research purposes in technical universities. The robot was built based on popular ultrasonic sensors to detect obstacles and a Raspberry Pi 4, which is a Linux-embedded computer. An effective obstacle avoidance algorithm for the robot was developed using a Bayesian neural network for classification. Training a Bayesian neural network does not require a validation dataset separate from the available data. In addition, the Bayesian approach can effectively handle the uncertainty of the system and result in the best generation for the network when inferring the unseen data. Training data are generated using robot-to-obstacle distances and the corresponding navigation modes. The commands to control the left and right motors of the robot are generated by the pretrained Bayesian neural network for three modes of navigation, forward, left, and right. Finally, this system can be useful as it can be conveniently integrated with advanced robot control algorithms.

Keywords:

autonomous mobile robots, obstacle avoidance, Bayesian neural network

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References

O. Esan, S. Du, and B. Lodewyk, "Review on Autonomous Indoor Wheel Mobile Robot Navigation Systems," in 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, Dec. 2020, pp. 1–6. DOI: https://doi.org/10.1109/icABCD49160.2020.9183838

S. Kumar, M. Majeedullah, A. B. Buriro, and Rohibullah, "Autonomous Navigation and Real Time Mapping Using Ultrasonic Sensors in NAO Humanoid Robot," Engineering, Technology & Applied Science Research, vol. 12, no. 5, pp. 9102–9107, Oct. 2022. DOI: https://doi.org/10.48084/etasr.5180

M. I. Ibrahim, N. Sariff, J. Johari, and N. Buniyamin, "Mobile robot obstacle avoidance in various type of static environments using fuzzy logic approach," in 2014 2nd International Conference on Electrical, Electronics and System Engineering (ICEESE), Kuala Lumpur, Malaysia, Sep. 2014, pp. 83–88. DOI: https://doi.org/10.1109/ICEESE.2014.7154600

L. Ren, W. Wang, and Z. Du, "A new fuzzy intelligent obstacle avoidance control strategy for wheeled mobile robot," in 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China, Dec. 2012, pp. 1732–1737. DOI: https://doi.org/10.1109/ICMA.2012.6284398

A. Pandey, R. K. Sonkar, K. K. Pandey, and D. R. Parhi, "Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller," in 2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India, Jan. 2014, pp. 39–41. DOI: https://doi.org/10.1109/ISCO.2014.7103914

S. H. A. Mohammad, M. A. Jeffril, and N. Sariff, "Mobile robot obstacle avoidance by using Fuzzy Logic technique," in 2013 IEEE 3rd International Conference on System Engineering and Technology, Shah Alam, Malaysia, Dec. 2013, pp. 331–335. DOI: https://doi.org/10.1109/ICSEngT.2013.6650194

Y. Chen, Y. Wang, and X. Yu, "Obstacle avoidance path planning strategy for robot arm based on fuzzy logic," in 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China, Sep. 2012, pp. 1648–1653. DOI: https://doi.org/10.1109/ICARCV.2012.6485438

B. Kasmi and A. Hassam, "Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7011–7017, Apr. 2021. DOI: https://doi.org/10.48084/etasr.4031

H. Medjoubi, A. Yassine, and H. Abdelouahab, "Design and Study of an Adaptive Fuzzy Logic-Based Controller for Wheeled Mobile Robots Implemented in the Leader-Follower Formation Approach," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6935–6942, Apr. 2021. DOI: https://doi.org/10.48084/etasr.3950

W. Budiharto, "Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network," Computational Intelligence and Neuroscience, vol. 2015, May 2015, Art. no. e745823. DOI: https://doi.org/10.1155/2015/745823

K. H. Chi and M. F. R. Lee, "Obstacle avoidance in mobile robot using Neural Network," in 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), Xianning, China, Apr. 2011, pp. 5082–5085. DOI: https://doi.org/10.1109/CECNET.2011.5768815

B. Ko, H. J. Choi, C. Hong, J. H. Kim, O. C. Kwon, and C. D. Yoo, "Neural network-based autonomous navigation for a homecare mobile robot," in 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju, Korea (South), Oct. 2017, pp. 403–406.

K. K. A. Farag, H. H. Shehata, and H. M. El-Batsh, "Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique," Journal of Robotics, vol. 2021, Nov. 2021, Art. no. e1129872. DOI: https://doi.org/10.1155/2021/1129872

D. J. C. MacKay, "The Evidence Framework Applied to Classification Networks," Neural Computation, vol. 4, no. 5, pp. 720–736, Sep. 1992. DOI: https://doi.org/10.1162/neco.1992.4.5.720

C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, UK: Clarendon Press, 1995. DOI: https://doi.org/10.1093/oso/9780198538493.001.0001

I. Nabney, NETLAB: Algorithms for Pattern Recognition. Berlin, Germany: Springer Science & Business Media, 2002.

M. F. Møller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, vol. 6, no. 4, pp. 525–533, Jan. 1993. DOI: https://doi.org/10.1016/S0893-6080(05)80056-5

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How to Cite

[1]
A. Hoang, S. T. Nguyen, T. V. Pham, T. M. Pham, L. V. Trieu, and T. T. Cao, “A Bayesian Neural Network-based Obstacle Avoidance Algorithm for an Educational Autonomous Mobile Robot Platform”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 6, pp. 12183–12189, Dec. 2023.

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Copyright (c) 2023 Tuan Van Pham, Son Thanh Nguyen, Anh Hoang, Tu Minh Pham; Linh Viet Trieu; Trung Thanh Cao

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