Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA)
Gundla Rajesh
Vignan's Institute of Management and Technology for Women
Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Multi-agent intelligence with privacy awareness for robust marine robotic electric drives Vishnu Kumar Mishra; Megha Mishra; T. Ram Kumar; Yenna Geetha Reddy; Sri Lavanya Sajja; Gundla Rajesh; Bandla Srinivasa Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1702-1713

Abstract

This paper introduces a privacy-preserving multi-agent intelligent model tailored towards improving the movement control and energy management of robotic marine electric motors. In particular, the research incorporates the use of a fractional-order neural network algorithm (FONNA) combined with federated learning, allowing for decentralization without compromising raw data security. The modeling process entails the application of a seven-phase voltage source inverter (VSI) to offer high-performance motor behavior and exceptional fault resilience. Some of the significant discoveries include the delivery of 96.96% peak efficiency at 55 kHz and decreasing dependency on high-bandwidth communication by 75% via gradient updates. Energy resilience remains an essential prerequisite for collaborative marine robotics in harsh offshore conditions where the electricity supply can be subjected to disruption. However, even with all these developments, robotic fleets for maritime purposes are faced with some major constraints when working together. For instance, the centralized approach is easily affected by system failures since all operations will be affected if one fails. The marine area poses harsh conditions in which communication bandwidth and electrical power may be difficult to provide.
Integrating large language models for context-aware decision making in autonomous mobile robots Vishnu Kumar Mishra; Megha Mishra; Talasila Ram Kumar; Yenna Geetha Reddy; Gundla Rajesh; Battula Phijik; Bandla Srinivasa Rao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp607-620

Abstract

The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Co-Authors Bandla Srinivasa Rao Battula Phijik Megha Mishra Megha Mishra Sri Lavanya Sajja T. Ram Kumar Talasila Ram Kumar Vishnu Kumar Mishra Yenna Geetha Reddy