Tomoaki Nakamura
Papers
5
Total Citations
79
H-Index
3
About
Tomoaki Nakamura is a robotics researcher whose work centers on autonomous mobile robot navigation, with a particular focus on local path planning, reinforcement learning, and motion planning in constrained environments. His research addresses one of the most challenging problems in mobile robotics: enabling robots to navigate efficiently through real-world spaces, including narrow corridors and congested areas that demand sophisticated decision-making. Nakamura's most influential contribution integrates the Dynamic Window Approach (DWA) with Q-learning to handle congestion-aware navigation, earning 38 citations since its 2023 publication. He has pioneered path planning strategies that explicitly account for turnabouts — complex maneuvers required when non-holonomic robots encounter spaces too tight for conventional navigation — leveraging Deep Q-Networks and Deep Deterministic Policy Gradient (DDPG) algorithms to solve these scenarios. His 2023 paper on DQN-based turnabout planning has attracted 29 citations, demonstrating the community's strong interest in this underexplored problem. With multiple high-impact publications released in a single year, Nakamura has rapidly established himself as an emerging voice in intelligent robot navigation. His work is particularly valuable for students and engineers developing service robots intended to operate safely and autonomously within human living environments.
Research Focus
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Top Papers
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