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

TL;DR

This paper presents a CNN-based system for predator robot navigation using both frame-based and event-driven data from a dynamic vision sensor, achieving high accuracy in real-time predator-prey scenarios.

Contribution

It introduces a mixed frame/event-driven CNN approach that operates at variable sampling rates, compatible with standard deep learning frameworks, for real-time robot steering.

Findings

Achieved up to 92% accuracy in closed-loop predator-prey trials.

Demonstrated effective real-time steering with variable scene activity.

Validated the approach on a Summit XL robot using DAVIS sensor data.

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.