Papers
15
Total Citations
137
H-Index
8
About
Kazutoshi Sukigara is a leading researcher in personal robotics, with a focus on developing intelligent mobility systems to support aging societies. His work centers on model predictive control (MPC) and posture stabilization for personal robots (PRs) and personal mobility robots (PMRs), particularly wheeled inverted pendulum platforms. Sukigara’s major contributions include pioneering human-following methods that ensure safe adjacency without collision, using MPC and deep neural network (DNN) policy learning to optimize control inputs under real-world constraints. He also advanced robust quick turning and standing-up control through initial value compensation (IVC), enhancing user safety and autonomy. His research has garnered over 120 citations, with his most cited paper (19 citations) addressing posture stabilization via lateral acceleration feedback. Notably, his work emphasizes balanced robot assistance—preventing physical deterioration in elderly users by promoting active lifestyles. Sukigara’s innovations in sensor fusion, including IR tag detection and omnidirectional camera tracking, further demonstrate his impact on human-robot interaction. His achievements highlight a commitment to creating robots that empower rather than replace human capabilities.
Research Focus
Key Achievements
Top Papers
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- 3MPC policy learning using DNN for human following control without collision16 citations · 2018
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- 6Personal robot assisting transportation to support active human life11 citations · 2015
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- 8Mode Switching Control for Personal Mobility Robot8 citations · 2011
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