Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot

Frontiers in Neurorobotics

Zhuangyi Jiang¹  Zhenshan Bing¹  Alois Knoll¹  Kai Huang² 

[1] Chair of Robotics, Artificial Intelligence and Real-time Systems, Department of Informatics, Technical University of Munich, Munich, Germany;Department of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China;

关键词: neuromorphic vision;    spiking neural network;    snake robot;    Hough transform;    target tracking;   

DOI  :  10.3389/fnbot.2019.00029

来源: DOAJ

【 摘 要 】

Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision sensor (NVS), which mimics the biological retina to detect a target at a higher temporal frequency and in a wider dynamic range. In this study, an NVS and a spiking neural network (SNN) were performed on a snake robot for the first time to achieve pipe-like object tracking. An SNN based on Hough Transform was designed to detect a target with an asynchronous event stream fed by the NVS. Combining the state of snake motion analyzed by the joint position sensors, a tracking framework was proposed. The experimental results obtained from the simulator demonstrated the validity of our framework and the autonomous locomotion ability of our snake robot. Comparing the performances of the SNN model on CPUs and on GPUs, respectively, the SNN model showed the best performance on a GPU under a simplified and synchronous update rule while it possessed higher precision on a CPU in an asynchronous way.

【 授权许可】

Unknown