Mutsumi Iwasa
Papers
5
Total Citations
21
H-Index
3
About
Mutsumi Iwasa is a robotics researcher whose work focuses on intelligent navigation and motion generation for autonomous and multi-legged robots operating in complex, real-world environments. A central theme of their research is path planning under uncertainty, particularly for disaster response scenarios where accurate environmental maps are unavailable. Iwasa’s major contributions include the development of a real-time rolling risk estimation method using fuzzy inference, which allows mobile robots to dynamically assess travel risk and plan safer routes. They have also advanced multi-legged robot locomotion by applying knowledge transfer techniques to simplify motion generation in rough terrain. To address the challenge of continuous state spaces in reinforcement learning, Iwasa proposed a Growing Neural Gas-based method for efficient state space construction. Their work on spatiotemporal graphs enables global path planning in environments with predictable moving obstacles, while their return-way path planning research tackles the critical problem of safe robot retrieval. With over 20 citations across their most-cited papers, Iwasa’s research provides practical solutions for autonomous navigation in hazardous and dynamic settings, contributing to the broader goal of deploying intelligent robots in real-world missions.
Research Focus
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Top Papers
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