Papers
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Total Citations
2
H-Index
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About
Kelash Kanwar is a researcher focused on advancing autonomous navigation and robot motion planning in complex, dynamic environments. His work addresses the critical challenge of enabling robots to safely and efficiently avoid collisions with moving obstacles, particularly when those obstacles’ future paths are unpredictable and subject to frequent change. Kanwar’s key contribution lies in developing algorithms that account for a robot’s kinodynamic constraints—such as limits on velocity, acceleration, and turning radius—while navigating among passive agents. His 2021 paper, “Collision Avoidance of a Kinodynamically Constrained System from Passive Agents,” lays foundational groundwork for this problem, proposing methods that allow robots to make real-time decisions even with short predictive horizons. Though early in his career, with his most-cited work accumulating 2 citations, Kanwar’s research is positioned at the intersection of robotics, control theory, and artificial intelligence, offering practical solutions for applications like autonomous vehicles, drones, and service robots. His work underscores the importance of robust, constraint-aware planning in ensuring safety and reliability in unpredictable settings, marking him as an emerging voice in the field of dynamic obstacle avoidance.
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