Abstract
Advanced control for mobile robotic platforms allows efficient real-time navigation in structured and unstructured environments in various industry applications. Deep reinforcement learning is an emerging control strategy where an agent is trained iteratively according to an optimisation objective by using reward and penalty actions. The agent generates the neural network weights used for computing the robot command towards the reference set point. We present an application for an open hardware mobile robotic platform navigation that integrates the sensing, communication, computing and control functions into a single system for navigation in unstructured environments. Implementation is performed through a dedicated software and communication layer that integrates the hardware platform with the MATLAB environment using standardized Robot Operating System (ROS) libraries. Quantitative testing results are presented, in order to prove the viability of the solution, by defining both simulation and laboratory setting scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Choi, J., Park, K., Kim, M., Seok, S.: Deep reinforcement learning of navigation in a complex and crowded environment with a limited field of view. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 5993–6000 (2019). https://doi.org/10.1109/ICRA.2019.8793979
Fu, Y., Jha, D.K., Zhang, Z., Yuan, Z., Ray, A.: Neural network-based learning from demonstration of an autonomous ground robot. Machines 7(2), 24 (2019). https://doi.org/10.3390/machines7020024, http://dx.doi.org/10.3390/machines7020024
Han, X.: A mathematical introduction to reinforcement learning (2018)
Li, Y.: Deep reinforcement learning: Opportunities and challenges. arXiv preprint arXiv:2202.11296 (2022)
Luchian, R.A., Rosioru, S., Stamatescu, I., Fagarasan, I., Stamatescu, G.: Enabling industrial motion control through IIoT multi-agent communication. In: IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society (2021)
Luchian, R.A., Stamatescu, G., Stamatescu, I., Fagarasan, I., Popescu, D.: IIoT decentralized system monitoring for smart industry applications. In: 2021 29th Mediterranean Conference on Control and Automation (MED), pp. 1161–1166 (2021)
Peng, Y., Zhang, X., Jiang, Y., Xu, X., Liu, J.: Leader-follower formation control for indoor wheeled robots via dual heuristic programming. In: 2020 3rd International Conference on Unmanned Systems (ICUS), pp. 600–605 (2020). https://doi.org/10.1109/ICUS50048.2020.9274823
Rosioru, S., Mihai, V., Neghina, M., Craciunean, D., Stamatescu, G.: PROSIM in the cloud: remote automation training platform with virtualized infrastructure. Appl. Sci. 12(6), 3038 (2022)
Shabbir, J., Anwer, T.: A survey of deep learning techniques for mobile robot applications. CoRR abs/1803.07608 (2018)
Wang, B.: Path planning of mobile robot based on a algorithm. In: 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI), pp. 524–528 (2021). https://doi.org/10.1109/ICETCI53161.2021.9563354
Acknowledgements
Financial support from the Competitiveness Operational Program 2014- 2020, Action 1.1.3: Creating synergies with RDI actions of the EU’s HORIZON 2020 framework program and other international RDI programs, MySMIS Code 108792, project acronym “UPB4H”, financed by contract: 250/11.05.2020 is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pavel, MD., Roșioru, S., Arghira, N., Stamatescu, G. (2023). Control of Open Mobile Robotic Platform Using Deep Reinforcement Learning. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2022. Studies in Computational Intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-24291-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24290-8
Online ISBN: 978-3-031-24291-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)