Smart Robotic Quadruped: Using Machine Learning for Optimization and Control
Smart Robotic Quadruped project focuses on applying machine learning to optimize parameters and control a robotic quadruped. The team tackles hardware challenges such as voltage, power, and torque, with backup plans in place. Software challenges include running simulations for learning and ensuring fault tolerance. Test plans involve hardware and software testing, along with code examples for implementation.
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Presentation Transcript
Smart Robotic Quadruped: Using Machine Learning to Optimize Parameters and Control Brittany Lamorie, Cody Otto, Duncan Prance
General Information Functionality: - Taught from computer - Milestones - Adjusts on the fly Motivations: - Machine Learning - Building a robot Inspiration: - Bipedal Simulations done in 2013 Proof of Concept: https://youtu.be/M8YjvHYbZ9w?t=15
Hardware Block Diagram
Hardware Challenges: - Voltage - Power - Torque Backup Plans: - Tethered - Higher torque/cost servos
Data Flow Diagram
Software Challenges: - Need to run large amounts of simulations for learning - Making sure that our code is redundant and fault tolerant - Not killing the fragile hardware Backup Plans: - Not using machine learning - Confining movement to a set boundary - Physical tethering for power requirements
Test Plan Hardware: - Test servo motion - Test single leg - Test weight with full setup Software: - Test robot fully in simulation software (pre-recorded) - Using working robot to test simulation results for accuracy - Setup learning algorithm and test that the parameters fall inside set boundaries
