Akshay Shetty
Papers
1
Total Citations
2
H-Index
1
About
Akshay Shetty is a researcher specializing in multi-robot systems, distributed optimization, and autonomous coordination under uncertainty. His work focuses on the critical challenge of maintaining reliable communication networks among robot teams operating in complex, real-world environments where motion and sensing uncertainties can compromise coordination. His most notable contribution, published in 2020, introduces a distributed ADMM-based trajectory planning framework that enables multi-robot systems to preserve network connectivity despite these uncertainties — a foundational problem for deploying robot teams on complex collaborative missions. By leveraging the Alternating Direction Method of Multipliers (ADMM) in a distributed fashion, Shetty's approach allows robots to plan trajectories cooperatively without relying on centralized computation, making the system both scalable and robust. This work has begun attracting citations within the robotics and autonomous systems community, reflecting its relevance to emerging applications in search and rescue, exploration, and cooperative surveillance. Shetty's research addresses a timely intersection of control theory, optimization, and multi-agent robotics, contributing practical algorithmic tools that bring resilient autonomous robot teams closer to real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1