Papers
6
Total Citations
39
H-Index
3
About
Eiji Inoue’s research bridges robotics, middleware, and autonomous systems, with a focus on enabling smarter human-robot collaboration and data acquisition. His most influential work, “A Data Acquisition Middleware” (2007, 16 citations), introduced DAQ-Middleware, a software framework built on RT-Middleware that simplifies distributed data acquisition across multiple PCs—a key contribution to both robotics and experimental physics. Inoue also advanced object recognition by integrating appearance models into RFID tags attached to the environment (2002, 12 citations), allowing robots to identify objects using stored models and learn from recognition failures. His practical engineering is evident in the development of a tracking laser rangefinder for a weed mowing robot (2017), which uses a camera and laser to measure position for autonomous navigation. Further work on DAQ-Middleware’s performance (2011) and control functionality (2014) solidified its utility, while his early exploration of master-assisted cooperative control (2002) applied neural networks to balance human and robot autonomy in hazardous tasks. With a career spanning middleware infrastructure to field robotics, Inoue’s contributions have shaped efficient, scalable systems for data handling and autonomous operation.
Research Focus
Key Achievements
Top Papers
- 1A Data Acquisition Middleware16 citations · 2007
- 2Object recognition using appearance models accumulated into environment12 citations · 2002
- 3
- 4Performance measurement of DAQ-Middleware3 citations · 2011
- 5Control functionality of DAQ-Middleware2 citations · 2014
- 6Master assisted cooperative control of human and robot2 citations · 2002