Amit Chakrabarti

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About

Amit Chakrabarti is a leading researcher in robotics and motion planning, with a focus on developing efficient algorithms for real-world autonomous systems. His key research areas include sampling-based motion planning, roadmap construction, and collision detection optimization. Chakrabarti’s most notable contribution is the introduction of a fast weighted streaming spanner algorithm (WSS), which dramatically improves the efficiency of roadmap generation for robot motion planning. By trimming edges from Probabilistic Roadmap (PRM) and its variants—such as k-PRM*—before collision detection, his method reduces computational overhead without sacrificing path quality. This work, published in 2015 and cited 19 times, has been influential in advancing real-time planning for complex robotic systems. Chakrabarti’s research bridges the gap between theoretical algorithm design and practical deployment, making him a respected figure in the robotics community. His contributions continue to inspire students and researchers seeking to push the boundaries of autonomous navigation and efficient motion planning.