Chandrashekhar Kumbhar
Papers
1
Total Citations
9
H-Index
1
About
Chandrashekhar Kumbhar is a robotics researcher whose work centers on intelligent motion planning and collision avoidance for autonomous mobile robots. His most-cited paper, "Trajectory Planning and Collision Control of a Mobile Robot: A Penalty‐Based PSO Approach" (2023), tackles the fundamental challenge of enabling robots to navigate dynamic environments safely and efficiently. In this work, Kumbhar introduces an adaptive Particle Swarm Optimization (PSO) algorithm enhanced with a penalty-based methodology, which effectively generates collision-free trajectories while optimizing path smoothness and travel time. With 9 citations, this paper has already garnered attention for its practical, computationally efficient approach to a core problem in mobile robotics. Kumbhar’s contributions lie at the intersection of swarm intelligence and real-time control, offering scalable solutions for autonomous navigation in cluttered or unpredictable settings. His research is particularly relevant for applications in warehouse logistics, service robotics, and autonomous vehicles. By combining bio-inspired optimization with robust collision constraints, Kumbhar is helping to bridge the gap between theoretical motion planning and real-world robotic deployment.
Research Focus
Key Achievements
Top Papers
- 1