Abhay Rawat

Papers

Total Citations

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About

Abhay Rawat is an emerging robotics researcher whose work centers on multi-robot systems, autonomous coordination, and machine learning-based control strategies. His research addresses some of the most pressing challenges in collaborative robotics, particularly how groups of robots can work together efficiently and adaptively in real-world scenarios. Rawat's most notable contribution, "Loosely Coupled Payload Transport System with Robot Replacement" (2019), introduces an innovative algorithm enabling seamless robot replacement within multi-robot teams, significantly extending the operational lifespan of payload transport systems using nonholonomic wheeled mobile robots. This work demonstrates a practical approach to one of robotics' core reliability challenges. Building on this foundation, his 2020 paper, "Multi-Robot Formation Control Using Reinforcement Learning," applies multi-agent reinforcement learning to develop intelligent control policies that allow robot teams to maintain precise formations while navigating toward target destinations — a critical capability for applications ranging from search-and-rescue to autonomous logistics. With a combined citation count reflecting growing recognition in the field, Rawat's research sits at the exciting intersection of classical robotics and modern machine learning. His contributions offer meaningful stepping stones for students and researchers exploring cooperative robotics, autonomous systems design, and AI-driven motion planning.