T. Kikuchi
Papers
5
Total Citations
59
H-Index
3
About
T. Kikuchi has made significant contributions to the field of autonomous mobile robotics, with a primary focus on robust localization and decision-making under uncertainty. Their most impactful work, "Expansion resetting for recovery from fatal error in Monte Carlo localization" (42 citations), introduced a novel method for recovering from catastrophic localization failures—a critical challenge in real-world robot navigation. This expansion resetting approach offered a more reliable alternative to traditional sensor resetting methods. Kikuchi further advanced the field by developing the real-time QMDP method, which enables robots to make optimal decisions even when their state estimation is uncertain, as demonstrated in their 2006 paper. Their research consistently addresses the practical problem of uncertainty in self-localization, proposing frameworks that allow robots to navigate effectively without requiring perfect prior knowledge of their environment. Kikuchi also contributed to multi-robot systems, developing specialized simulators that account for camera characteristics in RoboCup applications. Through their work on state-value functions and particle filters, Kikuchi has helped bridge the gap between theoretical localization algorithms and practical, real-time robotic navigation systems.
Research Focus
Key Achievements
Top Papers
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- 4Improvement of Color Recognition Using Colored Objects3 citations · 2006
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