Yuuki Nishio
Papers
1
Total Citations
9
H-Index
1
About
Yuuki Nishio is a robotics researcher whose work centers on intelligent motion planning and obstacle avoidance for autonomous systems. His key contributions lie at the intersection of model predictive control (MPC) and fuzzy logic, particularly in developing methods that allow robots to navigate dynamic environments safely. In his most cited work, "Moving obstacle avoidance control by fuzzy potential method and model predictive control" (2017, 9 citations), Nishio proposed a novel hybrid approach that combines the Fuzzy Potential Method—effective for handling a robot’s shape—with MPC, which accounts for the robot’s dynamics and the motion of obstacles. This integration enables more realistic and robust navigation in cluttered, changing spaces. Although his citation count is modest, the work is notable for addressing a critical gap in robotics: the simultaneous consideration of both geometric and dynamic constraints. Nishio’s research is particularly relevant for autonomous vehicles, mobile robots, and drones operating in unpredictable environments. His contributions exemplify the practical fusion of control theory and computational intelligence, offering a foundation for safer, more adaptive robotic movement.
Research Focus
Key Achievements
Top Papers
- 1