Dynamics and Optimization of Physical Processes in Information Systems Using Autonomous Mobile Robots and Multi-Agent Systems
| Authors | Nemala Jayasri1 , Pellakuri Vidyullatha2, , A. Saravanan3, Anil Kumar Muthevi4, K.P. Dinakaran5, Nageswara Rao Medikondu6 |
| Affiliations |
1Research Scholar, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522302, AP, India 2Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522302, AP, India 3Department of Electronics and Communication Engineering, Jaya Engineering College Chennai 602024, Tamilnadu, India 4Department of Computer Science and Engineering, Aditya University Surampalem-533437, AP, India 5Department of EEE, Panimalar Engineering College, Bangalore Trunk Road, Varadharajapuram, Poonamallee, Chennai-600123, India 6Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram-522302, AP, India |
| Е-mail | pvidyullatha@kluniversity.in |
| Issue | Volume 17, Year 2025, Number 3 |
| Dates | Received 12 April 2025; revised manuscript received 14 June 2025; published online 27 June 2025 |
| Citation | Nemala Jayasri, Pellakuri Vidyullatha, et al., J. Nano- Electron. Phys. 17 No 3, 03025 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(3).03025 |
| PACS Number(s) | 07.05.Mh, 07.07.Tw |
| Keywords | Autonomous Mobile Robots (AMRs), Multi-agent systems, Path planning, A Algorithm*, Real-time navigation and operational optimization. |
| Annotation |
Optimizing physical processes in information systems is crucial for enhancing the efficiency of autonomous mobile robots (AMRs) and multi-agent systems in dynamic environments. This study presents an advanced path planning and coordination approach that integrates AMRs with multi-agent strategies to improve real-time navigation and task execution. The A* (A-Star) algorithm is employed and enhanced with adaptive heuristic modifications to optimize travel time, energy efficiency, and operational throughput. A dynamic cost function is introduced to adjust path selection based on environmental constraints, obstacle distributions, and real-time system dynamics. Additionally, a multi-agent coordination framework is developed to facilitate seamless interaction among multiple robots, ensuring efficient task allocation and collision-free movement. Simulation results in structured and unstructured environments demonstrate that the proposed methodology significantly reduces travel time, enhances system-wide productivity, and optimizes physical process execution in industrial and service robotics applications. By integrating intelligent heuristic adjustments and adaptive multi-agent coordination, this approach provides a robust solution for real-time autonomous navigation and process optimization in complex, constrained environments. |
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