Graduate School of Science and Engineering Information and Computer Science

Intelligent Mechatro-Informatics Laboratory

Staff

HASHIMOTO Masafumi
[Professor]

Acceptable course
Master's degree course	✓
Doctoral degree course	✓

Telephone : +81-774-65-6410
mhashimo@mail.doshisha.ac.jp
Office : KE-208
Database of Researchers

TAKAHASHI Kazuhiko
[Professor]

Acceptable course
Master's degree course	✓
Doctoral degree course	✓

Telephone : +81-774-65-6434
katakaha@mail.doshisha.ac.jp
Office : KE-209
Database of Researchers

Research Contents

By fusing mechatronic systems, such as robots and vehicles, with information and communication technologies (ICT) and artificial intelligence (AI), we can realize smart and friendly mechatronic systems. This research laboratory studies the sensing, control, information processing, and the system integration which are the fundamental technologies to build the smart and friendly mechatronic systems. Our current research themes are shown below.

<1> Vehicle automation/mobile robotics

Autonomous vehicles and mobile robots have been developed in fields for labor-savings and dangerous work such as factories, ports, construction, agriculture, forestry, and industrial plants. Recently, they are expanding into fields in close contact with our daily lives such as autonomous cars in Intelligent Transportation Systems (ITS), service robots, rehabilitation robots, and crime prevention/security robots. To realize autonomous vehicles and mobile robots, our research focuses on the following topics:

Sensing systems:
Map building, recognition of vehicle motion, tracking of static and moving objects in surrounding environments, and multi-sensor fusion

Control and safety systems:
Autonomous navigation, Multi-robot cooperation, and fault-tolerant systems

<2> Intelligent control

To achieve intelligent human-machine systems, control, measurement and modeling of systems are important. In view of this point, we are researching the following topics.

Neural network control systems:
Both the flexibility and the learning ability of neural networks would be effective to achieve controlling of a wide class of system, e.g. dynamic systems and nonlinear systems. It is therefore important to clarify a method for designing a control system based on the neural networks and to evaluate its performance.

Man-machine system:
To develop intelligent robotic systems that operate flexibly by recognizing user's intentions, studies on hands-free control systems using physiological signals are conducted.

Keywords