Tatsuhiro IKEBE
Papers
1
Total Citations
2
H-Index
1
About
Dr. Tatsuhiro Ikebe is a robotics researcher whose work centers on advancing autonomous navigation and path planning for mobile robots. His primary contributions lie in the application of dynamic programming and reinforcement learning principles to real-world robotic systems. In his most cited work, "Implementation of Brute-Force Value Iteration for Mobile Robot Path Planning and Obstacle Bypassing" (2023), Dr. Ikebe demonstrated a novel approach to robot navigation by applying a computationally intensive brute-force value iteration algorithm. While value iteration is typically more expensive than heuristic search methods, his research shows that it can perfectly calculate the expected cost-to-go from any point in a state space, providing robots with a complete and optimal navigation map. This method allows for more robust obstacle bypassing and path planning in complex environments. Though his citation count is currently modest, his work represents a significant theoretical contribution to the field, offering a foundation for future research into more computationally efficient implementations of optimal control for autonomous systems. Dr. Ikebe’s research is particularly valuable for students and engineers interested in bridging the gap between classical control theory and modern robotic applications.
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Top Papers
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