Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot - Jiang, Zhuangyi; Bing, Zhenshan; Huang, Kai; Knoll, Alois (Frontiers in Neurorobotics, 2019)
Target tracking ability based on vision is crucial to a bio-inspired snake robot for exploring an unknown environment. However, the traditional vision modules of snake robots are difficult to overcome the image blur resulting from the periodic swing. A promising approach is to use the neuromorphic vision sensor (NVS) mimicking the biological retina to detect a target at the higher temporal frequency and in the 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 SNN model performances respectively on CPUs, GPUs, 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.