My Account | Careers | Downloads | Blog

PROJECT TITLE :

Visual Cue-Guided Rat Cyborg for Automatic Navigation [Research Frontier]

ABSTRACT:

A rat robot could be a sort of animal robots, where an animal is connected to a machine system via a brain-pc interface. Electrical stimuli will be generated by the machine system and delivered to the animal's brain to control its behavior. The sensory capability and versatile motion ability of rat robots highlight their potential benefits over mechanical robots. But, most existing rat robots need 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 search out 'human-attention-grabbing' 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 laptop and we have a tendency to develop some object detection/identification algorithms to permit objects of interest located. Next, we tend to make the rat robot perform a selected motion automatically in response to a detected object, like turning left. One stimulus does not allow the rat to perform a motion successfully. Inspired by the fact that humans typically give a series of stimuli to a rat robot, we develop a closed-loop model that problems a stimulus sequence automatically consistent with the state of the rat and the objects in front of it until the rat completes the motion successfully. So, the rat robot, that we have a tendency to talk over with as a rat cyborg, is able to move in keeping with the detected objects without the requirement for manual operations. The object detection methods and therefore the closed-loop stimulation model are evaluated in experiments, that demonstrate that our rat cyborg can accomplish human-specified navigation automatically.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : Learning Versatile Convolution Filters for Efficient Visual Recognition ABSTRACT: This article presents versatile filters that can be used to construct efficient convolutional neural networks, which are widely
PROJECT TITLE : Deep Visual Odometry with Adaptive Memory ABSTRACT: A novel deep visual odometry (VO) method that takes into account global information by selecting memory and refining poses is presented here. The currently available
PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : A Review of Single-Source Deep Unsupervised Visual Domain Adaptation ABSTRACT: Deep neural networks have been shown to perform exceptionally well across a broad spectrum of benchmark vision tasks as a result

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry