Elaris Computing Nexus
Optimal Path Planning of Swarm Robots Using Multi Agent Deep Reinforcement Learning in Dynamic Environments
Elaris Computing Nexus
Received On : 02 June 2025
Revised On : 06 September 2025
Accepted On : 30 September 2025
Published On : 18 October 2025
Volume 01, 2025
Pages : 181-194
Abstract
The problem of optimal path planning among swarm robots under dynamic conditions is a critical problem since obstacles cannot be predicted, inter-robots can collide and coordinated navigation is required. The traditional approaches to path planning, including A-, D-, potential field-, rapidly exploring random trees (RRT), and particle swarm optimization (PSO), are not always effective in the multi-agent dynamic environment, which results in inefficiency of trajectories and the higher probability of collisions. To overcome these difficulties, this paper suggests a Multi-Agent Deep Reinforcement Learning-based Swarm Path Planning (MADRL-SPP) framework, which would allow the swarm robots to navigate in an adaptive way, with zero collisions, and consuming minimal energy. The suggested MADRL-SPP framework describes every robot as an intelligent agent that interacts with the environment and other agents and benefits collective trajectories using the method of reward-based learning. The simulations that are undertaken using MATLAB are performed within dynamic operating environments, where obstacles are moving, the speed of the robots is heterogeneous and there are communication constraints. Performance analysis takes into account efficiency of the paths, the collision rate, convergence rate, the energy use and the scalability. Comparative analysis shows that MADRL-SPP is greatly superior to the traditional methods, such as A-, D-, potential field, RRT-, and PSO by being up to 32 percent more efficient in path, 45 percent less colliding, and converges quicker in dynamic conditions. The suggested framework can provide a scalable and powerful approach to real-time multi-agent navigation, which demonstrates the prospects of the combination of deep reinforcement learning with swarm robotics in intricate and unpredictable settings.
Keywords
Swarm Robotics, Multi-Agent Reinforcement Learning, Path Planning, Dynamic Environments, MATLAB Simulation, MADRL-SPP.
- J. Zhang, J. Wu, X. Shen, and Y. Li, “Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star,” International Journal of Advanced Robotic Systems, vol. 18, no. 5, Sep. 2021, doi: 10.1177/17298814211042730.
- Z. Liu, H. Liu, Z. Lu, and Q. Zeng, “A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots,” IEEE Access, vol. 9, pp. 20602–20621, 2021, doi: 10.1109/access.2021.3055231.
- Z. Yingkun, “Flight path planning of agriculture UAV based on improved artificial potential field method,” 2018 Chinese Control and Decision Conference (CCDC), pp. 1526–1530, Jun. 2018, doi: 10.1109/ccdc.2018.8407369.
- C. Zhao, Y. Zhu, Y. Du, F. Liao, and C.-Y. Chan, “A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 17910–17921, Oct. 2022, doi: 10.1109/tits.2022.3164391.
- E. F. Morales, R. Murrieta-Cid, I. Becerra, and M. A. Esquivel-Basaldua, “A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning,” Intelligent Service Robotics, vol. 14, no. 5, pp. 773–805, Nov. 2021, doi: 10.1007/s11370-021-00398-z.
- D. Han, B. Mulyana, V. Stankovic, and S. Cheng, “A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation,” Sensors, vol. 23, no. 7, p. 3762, Apr. 2023, doi: 10.3390/s23073762.
- H. Ju, R. Juan, R. Gomez, K. Nakamura, and G. Li, “Transferring policy of deep reinforcement learning from simulation to reality for robotics,” Nature Machine Intelligence, vol. 4, no. 12, pp. 1077–1087, Dec. 2022, doi: 10.1038/s42256-022-00573-6.
- J. Orr and A. Dutta, “Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey,” Sensors, vol. 23, no. 7, p. 3625, Mar. 2023, doi: 10.3390/s23073625.
- M. Tao, Q. Li, and J. Yu, “Multi-Objective Dynamic Path Planning with Multi-Agent Deep Reinforcement Learning,” Journal of Marine Science and Engineering, vol. 13, no. 1, p. 20, Dec. 2024, doi: 10.3390/jmse13010020.
- M. L. Betalo, S. Leng, A. Mohammed Seid, H. Nahom Abishu, A. Erbad, and X. Bai, “Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15610–15626, 2025, doi: 10.1109/tase.2025.3558945.
- T. Yang, Y. Cao, and G. Sartoretti, “Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning,” 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), pp. 71–77, Dec. 2023, doi: 10.1109/mrs60187.2023.10416797.
- N. el I. Bouaziz, O. Mechali, K. L. Besseghieur, and N. Achour, “Trajectory Planning for Autonomous Formation of Wheeled Mobile Robots via Modified Artificial Potential Field and Improved PSO Algorithm,” Unmanned Systems, vol. 12, no. 06, pp. 1085–1104, May 2024, doi: 10.1142/s2301385025500372.
- J. Chen, F. Ling, Y. Zhang, T. You, Y. Liu, and X. Du, “Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system,” Swarm and Evolutionary Computation, vol. 69, p. 101005, Mar. 2022, doi: 10.1016/j.swevo.2021.101005.
- N. Dong, L. Zhang, H. Zhou, X. Li, S. Wu, and X. Liu, “Two-Stage Fast Matching Pursuit Algorithm for Multi-Target Localization,” IEEE Access, vol. 11, pp. 66318–66326, 2023, doi: 10.1109/access.2023.3290031.
- C. Zhu, M. Dastani, and S. Wang, “A survey of multi-agent deep reinforcement learning with communication,” Autonomous Agents and Multi-Agent Systems, vol. 38, no. 1, Jan. 2024, doi: 10.1007/s10458-023-09633-6.
- R. Shen et al., “Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy,” Applied Energy, vol. 312, p. 118724, Apr. 2022, doi: 10.1016/j.apenergy.2022.118724.
- F. Ye, P. Duan, L. Meng, and L. Xue, “A hybrid artificial bee colony algorithm with genetic augmented exploration mechanism toward safe and smooth path planning for mobile robot,” Biomimetic Intelligence and Robotics, vol. 5, no. 2, p. 100206, Jun. 2025, doi: 10.1016/j.birob.2024.100206.
- A. Haldorai and U. Kandaswamy, “Dynamic Spectrum Handovers in Cognitive Radio Networks,” Intelligent Spectrum Handovers in Cognitive Radio Networks, pp. 111–133, 2019, doi: 10.1007/978-3-030-15416-5_6.
- G. Gokilakrishnan, V. M, R. Anushanjali, R. Subbiah, and A. H, “Analysis of the Requirement and Architecture for the Internet of Drones,” 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 2260–2268, Mar. 2023, doi: 10.1109/icaccs57279.2023.10112779.
CRediT Author Statement
The author reviewed the results and approved the final version of the manuscript.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics Declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of Data and Materials
The entire simulated dataset that has been analyzed and produced in the present research are accessible to the respective author by request. The data sets consist of robot paths, obstacle set ups, and calculated performance measures to create figures and tables in this paper.
Author Information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding Author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this Article
Kanev Boris Lisitsa, “Optimal Path Planning of Swarm Robots Using Multi Agent Deep Reinforcement Learning in Dynamic Environments”, Elaris Computing Nexus, pp. 181-194, 2025, doi: 10.65148/ECN/2025017.
Copyright
© 2025 Kanev Boris Lisitsa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.