Masumi Ishikawa
Papers
17
Total Citations
117
H-Index
6
About
Masumi Ishikawa is a robotics researcher whose work centers on autonomous mobile robot navigation, machine learning, and intelligent control systems. He is perhaps best known for his pioneering contributions to sewer inspection robotics, developing autonomous systems capable of navigating the challenging, landmark-sparse environment of underground pipe networks. His most cited work, "A study of an autonomous mobile robot for a sewer inspection system" (2007, 28 citations), laid important groundwork in this specialized domain, while complementary papers on stereo camera and laser scanner-based navigation demonstrated practical landmark detection techniques using manholes, inlets, and pipe joints for reliable localization. Beyond infrastructure robotics, Ishikawa has made notable contributions to machine learning applied to mobile systems. His research on the mnSOM (modular network Self-Organizing Map) framework explored sophisticated task segmentation strategies for robots, enabling intelligent division of complex behaviors into learnable modules. He also advanced reinforcement learning methodology by applying genetic algorithms to optimize learning parameters, reducing computational costs and improving navigational performance. His work on behavior emergence through existence-driven reinforcement learning reflects a broader interest in biologically inspired intelligence. Collectively, Ishikawa's research bridges practical robotics engineering with adaptive learning, offering meaningful contributions to autonomous systems operating in real-world, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1A study of an autonomous mobile robot for a sewer inspection system28 citations · 2007
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10