Name
Title | ||
|---|---|---|
Steering a predator robot using a mixed frame/event-driven convolutional neural network |
Abstract | ||
|---|---|---|
This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor “frames” that consist of a constant number of DAVIS ON and OFF events. The network is thus “data driven” at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/EBCCSP.2016.7605233 | 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP) |
Keywords | Field | DocType |
predator robot steering,mixed frame/event-driven convolutional neural network,CNN,predator/prey scenario,dynamic and active pixel sensor,Summit XL robot,image frames,dynamic vision sensor frames,DAVIS ONOFF events,steer right,steer left,steer center,nonvisible steer,on-board Summit XL robot,jAER,closed-loop trials,DAVIS event timing,deep learning technology,frequency 15 Hz to 240 Hz | Summit,Data-driven,Convolutional neural network,Simulation,Sampling (signal processing),CMOS sensor,Control engineering,Artificial intelligence,Labeled data,Engineering,Deep learning,Robot | Journal |
Volume | ISBN | Citations |
abs/1606.09433 | 978-1-5090-4197-8 | 13 |
PageRank | References | Authors |
1.00 | 5 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diederik Paul Moeys | 1 | 21 | 3.88 |
| Federico Corradi | 2 | 79 | 7.14 |
| Emmett Kerr | 3 | 25 | 4.10 |
| Philip J. Vance | 4 | 41 | 4.92 |
| Gautham P. Das | 5 | 15 | 2.72 |
| Daniel Neil | 6 | 192 | 11.78 |
| Dermot Kerr | 7 | 50 | 13.84 |
| Tobi Delbrück | 8 | 876 | 82.59 |