Details
Presenter(s)
![Xinyun Zou Headshot](https://confcats-catavault.s3.amazonaws.com/CATAVault/ieeecass/master/files/styles/cc_user_photo/s3/user-pictures/12861.png?h=db21fca1&itok=wNX61Dv8)
Display Name
Xinyun Zou
- Affiliation
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AffiliationUniversity of California, Irvine
- Country
Abstract
We developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types in a botanical garden. It included a recurrent layer and a supervised layer. The input spike trains to the recurrent layer were generated from linear accelerometer and gyroscope sensor values as well as camera frames from a ground robot. Compared to an SVM model and a 3L logistic regression model, our r-SNN method generated better prediction accuracy without reliance on a time window of data. Because the r-SNN is compatible with neuromorphic hardware, our proposed method could be part of a biologically-inspired power-efficient autonomous robot navigation system.