Papers
6
Total Citations
125
H-Index
4
About
Kunal Garg is a leading researcher at the forefront of safe autonomy and multi-robot coordination. His work centers on developing rigorous, theoretically grounded frameworks for control synthesis under complex constraints. Garg’s major contributions lie in advancing the theory of Control Barrier Functions (CBFs), where he has addressed critical practical challenges like time-varying safety constraints, input limits, and high-relative-degree systems. His highly cited 2024 tutorial (43 citations) and 2022 paper (37 citations) on fixed-time control using Quadratic Programming (QP) provide foundational tools for guaranteeing safety and performance in autonomous systems. Garg has also made significant strides in multi-agent systems, tackling the intractable problem of deadlock resolution through hierarchical control and exploring learning-based safe control methods for robot teams. His work on prescribed-time control (2019) further showcases his ability to handle spatiotemporal constraints. With a research portfolio that bridges theory and application, Garg is shaping how we design safe, reliable controllers for the next generation of autonomous and robotic systems.
Research Focus
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Top Papers
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