Sandesh Athni Hiremath
University of Koblenz and Landau, University of Kaiserslautern
Papers
3
Total Citations
14
H-Index
2
About
Sandesh Athni Hiremath is a robotics and control systems researcher whose work bridges model-based optimization and data-driven learning for autonomous mobile robots. His research centers on path following control, model predictive control (MPC), and deep neural network (DNN) based controllers, with applications ranging from omnidirectional robots to inverted pendulum systems. Hiremath’s most impactful contribution is a predictive path following controller for mobile robots that integrates automatic parameter tuning within the Frenet-Serret frame, enabling robust trajectory tracking without manual calibration (7 citations). He has also pioneered learning-based algorithms for state-constrained stochastic control, successfully applying them to a 2D cartpole system to generate optimal Markov control policies under uncertainty (5 citations). More recently, he introduced a novel Convolutional Vision Transformer that functions as a path following controller for omnidirectional robots, leveraging implicit Hankel structure enforcement to improve long-horizon prediction (2 citations). His work is notable for combining theoretical rigor—such as MPC formulations and stochastic optimal control—with practical implementations that reduce tuning effort and enhance real-time performance. With a growing citation record, Hiremath is establishing himself as a contributor to the next generation of intelligent, learning-augmented robotic control systems.
Research Focus
Key Achievements
Top Papers
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