Control Multi-Directional Mobile Robot Based on DDPG Intelligent Algorithm Application

International Journal of Electronics and Communication Engineering
© 2021 by SSRG - IJECE Journal
Volume 8 Issue 1
Year of Publication : 2021
Authors : Chau Thanh Phuong
How to Cite?

Chau Thanh Phuong, "Control Multi-Directional Mobile Robot Based on DDPG Intelligent Algorithm Application," SSRG International Journal of Electronics and Communication Engineering, vol. 8,  no. 1, pp. 18-23, 2021. Crossref,


The article, the implementation of the Deep Deterministic Policy Gradient algorithm on the Gazebo model and the reality of a multi-directional mobile robot, has been studied and applied. The empirical studies' goal is to make the multi-directional mobile robot learn the best possible action to travel in real-world environments when facing obstacles. When the robot moves in an environment with obstacles, the robot will automatically control to avoid these obstacles. Then, the more time it can remain within a specific limit, the more the reward is accumulated, and therefore the better results will be achieved. The author has performed various tests with many metamorphic parameters and proved that the DDPG algorithm is more efficient than algorithms like Q-learning, Machine learning, deep Q-network, etc. The research results will be the basis for designing and establishing control algorithms for present and future mobile multi-directional robots and industrial robots for application in programming engineering and home automation control industrial production machines. 


Multi-directional mobile robots, artificial intelligence, Obstacle robots, DDPG algorithm, autonomous navigation, reinforcement learning.


[1] Tran Hoai Linh, Neural network and its application in signal processing, Hanoi Polytechnic Publishing House, (2015).
[2] M.N. Cirstea, A. Dinu, J.G. Khor, M. McCormick, Neural and Fuzzy Logic Control of Drives and Power Systems,Linacre House, Jordan Hill, Oxford OX2 8DP, First published (2002).
[3] Nguyen Thanh Tuan, Base Deep learning, The Legrand Orange Book. Version 2, last update, (2020).
[4] Charu C. Aggarwal,Neural Networks and Deep Learning, Springer International Publishing AG, part of Springer Nature, (2018).
[5] Nils J. Nilsson, The quest for artificial intelligence a history of ideas and achievements, Web Version Print version published by Cambridge University Press, Publishing 13(2010),
[6] Vu Thi Thuy Nga, Ong Xuan Loc, Trinh Hai Nam, Enhanced learning in automatic control with Matlab Simulink, Hanoi Polytechnic Publishing House, (2020).
[7] Mohit Sewak, Deep Reinforcement Learning, Springer Nature Singapore Pte Ltd.( 2019).
[8] Latombe, J.C.,Robot Motion Planning, Kluwer Academic Publishers: Norwell, MA, USA, (1992).
[9] Han, J., and Seo, Y.,Mobile robot path planning with surrounding point set and path improvement, Appl. Soft Comp. 57(2018) 35–47.
[10] V. Matt and N. Aran.,Deep reinforcement learning approach to autonomous driving, ed: arXiv, (2017).
[11] Andrea Bacciotti,Stability and Control of Linear Systems,Publishing Ltd; Springer Nature Switzerland AG,(2019).
[12] L. Xin, Q. Wang, J. She, and Y. Li, Robust adaptive tracking control of wheeled mobile robot, Robotics and Autonomous Systems, 78(2016) 36–48.
[13] Bakdi, A., Hentout, A., Boutami, H., Maoudj, A., Hachour, O., and Bouzouia, B.,Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control, Robot. Autonomous Syst. 89(2017) 95–109.
[14] I. Zamora, N. G. Lopez, V. M. Vilches, and A. H. Cordero,
“Extending the open-air gym for robotics: A toolkit for reinforcement learning using ros and gazebo, arXiv preprint arXiv:1608.05742, (2016).
[15] Gu, S.; Holly, E.; Lillicrap, T.; Levine, S.,Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates.In Proceedings of the 2017 IEEE International Conference on Robotics and Automation, Marina Bay Sands, Singapore,(2017) 3389–3396, 29 May–2.
[16] A. D. Pambudi, T. Agustinah, and R. Effendi.,Reinforcement Point and Fuzzy Input Design of Fuzzy Q-Learning for Mobile Robot Navigation System,International Conference of Artificial Intelligence and Information Technology, (2019).
[17] Do Quang Hiep, Ngo Manh Tien, Nguyen Manh Cuong, Pham Tien Dung, Tran Van Manh, Nguyen Tien Kiem, Nguyen Duc Duy,An Approach to Design Navigation System for Omnidirectional Mobile Robot Based on ROS, (IJMERR);11(9)(2020) 1502-1508.
[18] X. Ruan, D. Ren, X. Zhu, and J. Huang,Mobile Robot Navigation based on Deep Reinforcement Learning, 2019 Chinese Control And Decision Conference (CCDC),(2019).
[19] N. Navarro-Guerrero, C. Weber, P. Schroeter, and S. Wermter,Real-world reinforcement learning for an autonomous humanoid robot, Robotics and Autonomous Systems,(2012).
[20] Saleem, Y.; Yau, K.L.A.; Mohamad, H.; Ramli, N.; Rehmani, M.H.; Ni, Q.,Clustering and Reinforcement Learning-Based Routing for Cognitive Radio Networks.,IEEE Wirel. Commun.(2017).
[21] Z. Miljković, M. Mitić, M. Lazarević, and B. Babić.,Neural network reinforcement learning for visual control of robot manipulators, Expert Systems with Applications,40(2013) 1721–1736.
[22] Pham Ngoc Sam, Tran Duc Chuyen,Research and Designing a Positioning System, Timeline Chemical Mapping for Multi-Direction Mobile Robot,7(11)(2020) 7-12, Publishing by SSRG - IJECE Journal,.
[23] Fu X, Du J, Guo Y, Liu M, Dong T, et al.,A machine learning framework for stock selection., arXiv, cited (2018).
[24] Shota Ohnishi, Eiji Uchibe, Yotaro Yamaguchi, Kosuke Nakanishi, Yuji Yasui, and Shin Ishii,constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning,13(2019) 7-12, Publishing by Frontiers in Neurorobotics Journal,.
[25], (2020).
[26] Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A.A.; Veness, J.; Bellemare, M.G., Graves, A.; Riedmiller, M.; Fidjeland, A.K.; Ostrovski, G.; et al.,Human-level control through deep reinforcement learning., Nature(2015).
[27] Ganggang Guo, Yulei Rao, Feida Zhu, Fang Xu.,Innovative deep matching algorithm for stock portfolio selection using deep stock profiles,PLoS One. 15(11)(2020) Published online 2020 November.
[28] Wang, C.; Zhang, Q.; Tian, Q.; Li, S.; Wang, X.; Lane, D.; Petillot, Y.; Wang, S.,Learning Mobile Manipulation through Deep Reinforcement Learning., Sensors (2020).
[29] Evan Prianto, MyeongSeop Kim, Jae-Han Park, Ji-Hun Bae, and Jung-Su Kim,Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor-Critic with Hindsight Experience Replay,Sensors, Published: 19(2020).