Mohammad Deghat

About

Mohammad Deghat is a leading researcher in robotics and multi-agent systems, whose work spans target localization, formation control, and the integration of AI for robot safety. His most influential contribution, the 2012 paper "Target localization and circumnavigation by a non-holonomic robot" (55 citations), addresses a fundamental surveillance problem: enabling an agent with only bearing angle information to achieve circular motion around a target—a critical capability for autonomous monitoring and tracking. Deghat has also advanced cooperative robotics, developing robust-adaptive controllers for dual robot manipulators carrying rigid payloads (2009), and pioneering iterative state feedback control for repetitive nonlinear systems. His recent work pushes boundaries in multi-agent formation control, introducing elevation angle-based methods that eliminate the need for a global coordinate frame (2024). Demonstrating his forward-looking approach, Deghat explores how Large Language Models and knowledge graphs can enhance robot safety through few-shot learning (2024). With a career spanning foundational control theory to cutting-edge AI integration, his research continues to shape how robots perceive, cooperate, and operate safely in complex environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Target localization and circumnavigation by a non-holonomic robot

   55 citations · 2012
2. 2
   Experimental study of a robust-adaptive controller design for two cooperating RLED robot manipulators carrying a rigid payload

   7 citations · 2009
3. 3
   Iterative state feedback control and its application to robot control

   4 citations · 2009
4. 4
   Bearing-Based Leader-Follower Formation Tracking Control Using Elevation Angle

   2 citations · 2024
5. 5
   How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach

   2 citations · 2024

Key Collaborators

Contact & Links