Vishal Chandran

Papers

Total Citations

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About

Vishal Chandran is a researcher at the forefront of intelligent robotics and autonomous systems, with a focused expertise in applying reinforcement learning to real-world navigation challenges. His most-cited work, "Autonomous Driving Mobile Robot using Q-learning" (2022), tackles the notoriously difficult problem of obstacle avoidance by implementing a Q-learning framework that enables mobile robots to make adaptive, collision-free decisions in dynamic environments. This contribution is particularly significant because it bridges the gap between theoretical reinforcement learning algorithms and practical robotic deployment—a long-standing hurdle in the field. With 3 citations, his study has already sparked interest among peers working on learning-based control, and it serves as a foundational reference for researchers exploring model-free approaches to autonomous navigation. Chandran’s work is notable for its clear demonstration of how trial-and-error learning can replace traditional, hand-coded path planning, offering a scalable solution for everything from warehouse logistics to assistive robotics. As the demand for intelligent, self-driving machines grows, his research continues to influence the next wave of adaptive robotic systems.