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About

Masanori Sekiguchi is a robotics researcher specializing in inverse kinematics, motion planning, and robot kinematics formulation for redundant and complex robotic systems. His major contributions include developing a fast and robust numerical method for inverse kinematics with prioritized multiple targets, which uses a virtual spring model and joint-based damping control to efficiently handle redundant robots (21 citations). He also introduced an extended angle-axis vector approach to avoid deadlock caused by joint limits, enhancing the reliability of inverse kinematics in constrained environments (13 citations). Sekiguchi’s work extends to formulating robot kinematics for diverse platforms—mobile manipulators, parallel link robots, and legged robots—using a unified tree-structured rigid-body system approach. Notable achievements include motion planning techniques that leverage geometrical properties of the entire robot body to generate obstacle-avoiding joint trajectories. His research, though early in impact, demonstrates significant potential for advancing autonomous and cooperative robotic systems, particularly in industrial and service applications where precision and adaptability are critical.