Mohammed Jaseem M

Papers

Total Citations

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About

Mohammed Jaseem M is a researcher whose work sits at the intersection of robotics and artificial intelligence, with a primary focus on autonomous mobile robot navigation. His most-cited paper, "Reinforcement Learning Based Approach For Mobile Robot Navigation" (2019, 8 citations), addresses a critical challenge in the field: the limitations of classical and heuristic navigation algorithms, which often become computationally tedious or trapped in local optima as environments grow more complex. By applying reinforcement learning, Jaseem proposes a more adaptive, intelligent framework for wheeled mobile robots to navigate dynamic and unstructured spaces. This contribution is particularly significant for advancing real-world applications in service robotics, warehouse automation, and autonomous exploration. While his citation count reflects a focused, emerging impact, his work signals a shift toward data-driven, learning-based solutions over traditional path-planning methods. For students and researchers exploring the intersection of machine learning and robotics, Jaseem’s research offers a clear, practical demonstration of how reinforcement learning can overcome the inherent rigidity of classical navigation systems.