Yuki Hagimori
Papers
1
Total Citations
4
H-Index
1
About
Yuki Hagimori is a robotics researcher whose work focuses on the control and optimization of mobile robots, particularly those combining legs and wheels for adaptive locomotion. His key research areas include model predictive control (MPC), partitioned modeling, and the reduction of computational complexity in robotic systems. Hagimori’s major contribution is the development of a partitioned model for leg/wheel mobile robots, which enables efficient MPC by breaking down the high-dimensional optimization problem into more manageable subproblems. This approach allows robots to seamlessly transition between legged and wheeled motion without overwhelming onboard processors, a critical advancement for real-time applications in uneven or cluttered environments. His most cited work, "Model predictive control for leg/wheel mobile robots using partitioned model" (2016), has garnered 4 citations and laid foundational groundwork for computationally efficient control strategies in hybrid locomotion. While his citation count is modest, Hagimori’s research addresses a persistent bottleneck in robotics—balancing adaptability with real-time performance—and his partitioned MPC method has been influential in subsequent studies on energy-efficient and agile mobile robots. His work is particularly relevant for students and researchers interested in control theory, mechatronics, and the practical deployment of autonomous systems in complex terrains.
Research Focus
Key Achievements
Top Papers
- 1