| Project/Area Number |
01550199
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
機械力学・制御工学
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
INABA Masayuki The University of Tokyo, Department of Mechanical Engineering Associate Professor, 工学部, 助教授 (50184726)
|
|---|
| Project Period (FY) |
1989 – 1990
|
| Project Status |
Completed (Fiscal Year 1990)
|
| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1990: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1989: ¥1,500,000 (Direct Cost: ¥1,500,000)
|
|---|
| Keywords | Neural Network / Robot Vision / Backpropagation / Pattern Recognition / Sensor Fusion / Robot Control / Parallel Processing / Transputer / ロボット / センサ- / 適応制御 |
|---|
| Research Abstract |
The purpose of this research is to study techniques of neural network system for sensor fusion and adaptive control of intelligent robot and develop an neural networks simulator on parallel distributed system of multiple transfers. The following summarize the several topics in this research.
1. Development of Neural Net Simulation System on Multiple Transputer System with Sensor Interface. The simulator uses eight transputers with 4MB for neural net calculation. One board in this system has one transputer T800 with 1MB memory and 32bit memory-mapped I/O to make interface with sensor devices. In order to develop several kind of neural works on this simulator, multi-lisp system on multi-transputer is developed.
2. Experiment of pattern recognition with several human face images. This aims checking the performance of the simulation system. Each image has the size of about 64 by 64. It takes few minutes to finish learning of the images of six persons.
3. Experiment of neural networks for motion control with feedforward and feedback. The input data to neural networks with three layers are the goal reference velocity to the robot control system, the past output torque and the past actual velocity. The teaching data to the network is the goal torque of the robot system. As the past data of output torque and velocity are also feedbacked to the networks, the network can learns feedback control process model in this configuration. In the experiment, a simple mobile robot which has two degree of freedom work under this networks.
4. Neural networks simulation to correlate sensor information derived from different kind of sensors. In this networks, several layered neural networks give its output signal to another network as teaching data. In order to separate the substantial information between difference sensors, an independent factor is defined and an expansion method of the network output is introduced.
|
