Sai Sravan Manne
Papers
1
Total Citations
10
H-Index
1
About
Sai Sravan Manne is a robotics researcher specializing in motion planning and control for autonomous systems, with a focus on non-holonomic robots. His most-cited work, "Comparison of Kinematic and Dynamic Model Based Linear Model Predictive Control of Non-Holonomic Robot for Trajectory Tracking: Critical Trade-offs Addressed" (2019), introduces a hierarchical control architecture that combines a model predictive controller (MPC) in the outer loop with a PI controller in the inner loop. This approach systematically addresses key trade-offs between kinematic and dynamic modeling for trajectory tracking, offering a practical solution to improve robot precision and stability. With 10 citations, this paper has influenced subsequent studies in autonomous navigation and control system design. Manne’s contributions are particularly valuable for students and researchers exploring MPC applications in robotics, as his work bridges theoretical modeling with real-world implementation challenges. His research underscores the importance of balancing computational efficiency and tracking accuracy, making him a notable figure in the field of autonomous vehicle and robot control.
Research Focus
Key Achievements
Top Papers
- 1