Kazuma Sekiguchi
Papers
17
Total Citations
79
H-Index
6
About
Kazuma Sekiguchi is a robotics researcher whose work centers on advanced motion planning, control systems, and autonomous navigation for mobile and legged robots. His primary contributions lie in the development and application of Model Predictive Control (MPC) frameworks to complex robotic systems, consistently demonstrating how optimization-based approaches can address real-world challenges in mobility, safety, and precision. Sekiguchi has made notable advances in obstacle avoidance, combining MPC with fuzzy potential methods to account for robot dynamics and environmental uncertainty, work that has attracted repeated citation since 2014. His research into localization — including moving horizon estimation integrating laser range sensors and odometry — has addressed critical challenges in autonomous robot safety, particularly in feature-sparse environments prone to singular localization failure. More recently, he has extended MPC techniques to mobile manipulators and active sensing for SLAM improvement, broadening the practical reach of his methods. His explorations also encompass leg/wheel hybrid robots for rough-terrain traversal and vertical jumping motion control for underactuated systems, reflecting a wide mechanical curiosity. With citations accumulating steadily across a decade of publications, Sekiguchi's body of work represents a sustained and technically rigorous contribution to intelligent, adaptive robotic control.
Research Focus
Key Achievements
Top Papers
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- 7Vertical jumping motion control for 4-link robot5 citations · 2011
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- 9Active sensing control improving SLAM accuracy for a vehicle robot4 citations · 2022
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