Fig 7 : Simulation result of controlling and navigating in
multiple different environments with obstacles.
Acknowledgments: We are thankful to University of Lahore for
providing us the equipped laboratory and my supervisor to complete this
research.
Conflict of interest: The authors declare no conflict of
interest.
Data availability statement: The data that support the findings
of this study are available from the corresponding author upon
reasonable request.
References
- Pedersen I, Reid S, Aspevig K. Developing social robots for aging
populations: A literature review of recent academic sources. Sociology
Compass. 2018 Apr 26;12(6):e12585.
- Wang M, Pan C, Ray PK. Technology Entrepreneurship in Developing
Countries: Role of Telepresence Robots in Healthcare. IEEE Engineering
Management Review. 2021 Mar;49(1):20–6.
- Wang J, Huang H, Li K, Li J. Towards the Unified Principles for Level
5 Autonomous Vehicles. Engineering. 2021 Jan;
- Zhou Z, Zhu P, Zeng Z, Xiao J, Lu H, Zhou Z. Robot navigation in a
crowd by integrating deep reinforcement learning and online planning.
Applied Intelligence. 2022 Mar 17.
- Zong C, Ji Z, Tian L, Zhang Y. Distributed Multi-Robot Formation
Control Based on Bipartite Consensus With Time-Varying Delays. IEEE
Access. 2019;7:144790–8.
- Van Houdt G, Mosquera C, Nápoles G. A review on the long short-term
memory model. Artificial Intelligence Review. 2020 May 13.
- Xu J, Zhang H, Qiu J. A deep deterministic policy gradient algorithm
based on averaged state-action estimation. Computers and Electrical
Engineering. 2022 Jul;101:108015.