Sponsor
Portland State University. Department of Mechanical and Materials Engineering
First Advisor
Alexander Hunt
Date of Publication
Spring 7-15-2019
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Mechanical Engineering
Department
Mechanical and Materials Engineering
Language
English
Subjects
Robotics, Robots -- Design, Robots -- Motion, Neural networks (Computer science), Mechanical engineering
DOI
10.15760/etd.7014
Physical Description
1 online resource (xii, 81 pages)
Abstract
Legged locomotion is a feat ubiquitous throughout the animal kingdom, but modern robots still fall far short of similar achievements. This paper presents the design of a canine-inspired quadruped robot named DoggyDeux as a platform for synthetic neural network (SNN) research that may be one avenue for robots to attain animal-like agility and adaptability. DoggyDeux features a fully 3D printed frame, 24 braided pneumatic actuators (BPAs) that drive four 3-DOF limbs in antagonistic extensor-flexor pairs, and an electrical system that allows it to respond to commands from a SNN comprised of central pattern generators (CPGs). Compared to the previous version of this robot, DoggyDeux eliminates out-of-plane bending moments on the legs, increases the range of motion of each joint, and eliminates buckling of the BPAs by utilizing a biologically inspired muscle attachment approach. A simple SNN comprised of a single isolated CPG for each joint is used to control the front left leg on DoggyDeux and joint angle data from this leg is collected to verify that the robot responds correctly to inputs from its SNN. Future design work on DoggyDeux will involve further improving the muscle attachment mechanism, while future SNN research will include expanding the robot's SNN to achieve coordinated locomotion with all four legs utilizing sensory feedback.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/29613
Recommended Citation
Scharzenberger, Cody Warren, "Design of a Canine Inspired Quadruped Robot as a Platform for Synthetic Neural Network Control" (2019). Dissertations and Theses. Paper 5135.
https://doi.org/10.15760/etd.7014