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Visual Cue-Guided Rat Cyborg

Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

A rat robot is a type of animal robot in which an animal is connected to a machine system via a brain-computer interface (BCI). Electrical stimuli can be generated by the machine system and delivered to the animal’s brain to control its behavior. However, most existing rat robots require that a human observes the environmental layout to guide navigation, which limits the applications of rat robots. This work incorporates object detection algorithms to a rat robot system to enable it to find ‘human-interesting’ objects, and then use these cues to guide its behaviors to perform automatic navigation. A miniature camera is mounted on the rat’s back to capture the scene in front of the rat. The video is transferred via a wireless module to a computer and we develop some object detection/identification algorithms to allow objects of interest to be found. Next, we make the rat robot perform a specific motion automatically in response to a detected object, such as turning left. A single stimulus does not allow the rat to perform a motion successfully. Inspired by the fact that humans usually give a series of stimuli to a rat robot, we develop a closed-loop model that issues a stimulus sequence automatically according to the state of the rat and the objects in front of it until the rat completes the motion successfully. Thus, the rat robot, which we refer to as a rat cyborg, is able to move according to the detected objects without requiring manual operations. The closed-loop stimulation model is evaluated in experiments, which demonstrate that our rat cyborg can accomplish human-specified navigation automatically.

Keywords

Rat cyborg Navigation Brain-computer interface 

Notes

Acknowledgements

This work was supported by the grants from the National 973 Program (no. 2013CB329500), National Natural Science Foundation of China (No. 61673340) and Zhejiang Provincial Natural Science Foundation of China (LZ17F030001, LR15F020001)

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Qiushi Academy for Advanced StudiesZhejiang UniversityHangzhouChina
  2. 2.College of Computer ScienceZhejiang UniversityHangzhouChina

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