Name
Abstract | ||
|---|---|---|
We have designed a visually guided collision warning system with a neuromorphic architecture, employing an algorithm inspired by the visual nervous system of locusts. The system was implemented with mixed analog–digital integrated circuits consisting of an analog resistive network and field-programmable gate array (FPGA) circuits. The resistive network processes the interaction between the laterally spreading excitatory and inhibitory signals instantaneously, which is essential for real-time computation of collision avoidance with a low power consumption and a compact hardware. The system responded selectively to approaching objects of simulated movie images at close range. The system was, however, confronted with serious noise problems due to the vibratory ego-motion, when it was installed in a mobile miniature car. To overcome this problem, we developed the algorithm, which is also installable in FPGA circuits, in order for the system to respond robustly during the ego-motion. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neunet.2008.10.003 | Neural Networks |
Keywords | Field | DocType |
Collision avoidance,Robot vision,Bio-inspired,Insect vision | Warning system,Logic gate,Neuromorphic engineering,Artificial intelligence,Computer hardware,Integrated circuit,Simulation,Field-programmable gate array,Collision,Gate array,Electronic circuit,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 10 | 0893-6080 |
Citations | PageRank | References |
1 | 0.36 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hirotsugu Okuno | 1 | 26 | 5.15 |
| Tetsuya Yagi | 2 | 140 | 27.73 |