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About

Dinsha Vinod is a researcher at the forefront of autonomous systems and control theory, whose work bridges the critical gap between real-time robotic perception and computational efficiency. Her primary research areas include fog-cloud computing for mobile robotics, model predictive control (MPC), and event-triggered control systems. Vinod’s most impactful contribution is the development of an autonomous fog computing platform that leverages control-theoretic approaches to enable real-time AI inferencing for robot-vision applications—a foundational paper that has garnered 15 citations. She further advanced this field with a data-driven MPC framework for fog-cloud platforms, addressing the challenge of processing vision data offloaded from mobile robots to enhance navigation performance. Her work on MPC-based navigation for omni-directional mobile robots under actuator failure demonstrates her focus on resilient, fault-tolerant systems. More recently, Vinod has pioneered event-triggered and self-triggered control strategies for discrete polytopic linear parameter-varying systems, reducing controller update frequency while maintaining performance. With a growing citation record and publications spanning 2022 to 2025, Vinod is establishing herself as a key innovator in the intersection of control theory, AI, and autonomous robotics.