Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network

Understand

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.