Artificial Neural Network-Based Robotic Control

<p>Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2018

Author dc:creator

• Ng, Justin

Contributors dc:contributor

• Andrew Danowitz

• Computational Engineering
• Controls and Control Theory
• Robotics

Identifier

10.15368/theses.2018.58

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3194