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Full-Text Articles in Engineering

A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan Jan 2020

A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan

Open Access Theses & Dissertations

Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has ...


A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal Jan 2020

A Comparative Analysis Of Reinforcement Learning Applied To Task-Space Reaching With A Robotic Manipulator With And Without Gravity Compensation, Jonathan Fugal

Theses and Dissertations--Electrical and Computer Engineering

Advances in computing power in recent years have facilitated developments in autonomous robotic systems. These robotic systems can be used in prosthetic limbs, wearhouse packaging and sorting, assembly line production, as well as many other applications. Designing these autonomous systems typically requires robotic system and world models (for classical control based strategies) or time consuming and computationally expensive training (for learning based strategies). Often these requirements are difficult to fulfill. There are ways to combine classical control and learning based strategies that can mitigate both requirements. One of these ways is to use a gravity compensated torque control with reinforcement ...


Exploring The Behavior Repertoire Of A Wireless Vibrationally Actuated Tensegrity Robot, Zongliang Ji Jun 2019

Exploring The Behavior Repertoire Of A Wireless Vibrationally Actuated Tensegrity Robot, Zongliang Ji

Honors Theses

Soft robotics is an emerging field of research due to its potential to explore and operate in unstructured, rugged, and dynamic environments. However, the properties that make soft robots compelling also make them difficult to robustly control. Here at Union, we developed the world’s first wireless soft tensegrity robot. The goal of my thesis is to explore effective and efficient methods to explore the diverse behavior our tensegrity robot. We will achieve that by applying state-of-art machine learning technique and a novelty search algorithm.


Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford May 2019

Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford

Theses and Dissertations

This thesis entails motor control system analysis, design, and optimization for the University of Arkansas NASA Robotic Mining Competition robot. The open-loop system is to be modeled and simulated in order to achieve a desired rapid, yet smooth response to a change in input. The initial goal of this work is to find a repeatable, generalized step-by-step process that can be used to tune the gains of a PID controller for multiple different operating points. Then, sensors are to be modeled onto the robot within a feedback loop to develop an error signal and to make the control system self-corrective ...


Learning Probabilistic Generative Models For Fast Sampling-Based Planning, Jinwook Huh Jan 2019

Learning Probabilistic Generative Models For Fast Sampling-Based Planning, Jinwook Huh

Publicly Accessible Penn Dissertations

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes ...


Integration Of Robotic Perception, Action, And Memory, Li Yang Ku Oct 2018

Integration Of Robotic Perception, Action, And Memory, Li Yang Ku

Doctoral Dissertations

In the book "On Intelligence", Hawkins states that intelligence should be measured by the capacity to memorize and predict patterns. I further suggest that the ability to predict action consequences based on perception and memory is essential for robots to demonstrate intelligent behaviors in unstructured environments. However, traditional approaches generally represent action and perception separately---as computer vision modules that recognize objects and as planners that execute actions based on labels and poses. I propose here a more integrated approach where action and perception are combined in a memory model, in which a sequence of actions can be planned based on ...


Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi Jan 2018

Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi

Electronic Theses and Dissertations

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage ...


Belief-Space Planning For Resourceful Manipulation And Mobility, Dirk Ruiken Jul 2017

Belief-Space Planning For Resourceful Manipulation And Mobility, Dirk Ruiken

Doctoral Dissertations

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use ...


Grounding Robot Motion In Natural Language And Visual Perception, Scott Alan Bronikowski Apr 2016

Grounding Robot Motion In Natural Language And Visual Perception, Scott Alan Bronikowski

Open Access Dissertations

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks.

I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct ...


Learning Parameterized Skills, Bruno Castro Da Silva Mar 2015

Learning Parameterized Skills, Bruno Castro Da Silva

Doctoral Dissertations

One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills.

In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and ...


Designing A Biomimetic Testing Platform For Actuators In A Series-Elastic Co-Contraction System, Ryan Tyler Schroeder Dec 2014

Designing A Biomimetic Testing Platform For Actuators In A Series-Elastic Co-Contraction System, Ryan Tyler Schroeder

UNLV Theses, Dissertations, Professional Papers, and Capstones

Actuators determine the performance of robotic systems at the most intimate of levels. As a result, much work has been done to assess the performance of different actuator systems. However, biomimetics has not previously been utilized as a pretext for tuning a series elastic actuator system with the purpose of designing an empirical testing platform. Thus, an artificial muscle tendon system has been developed in order to assess the performance of two distinct actuator types: (1) direct current electromagnetic motors and (2) ultrasonic rotary piezoelectric motors. Because the design of the system takes advantage of biomimetic operating principles such as ...


3d Robotic Sensing Of People: Human Perception, Representation And Activity Recognition, Hao Zhang Aug 2014

3d Robotic Sensing Of People: Human Perception, Representation And Activity Recognition, Hao Zhang

Doctoral Dissertations

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.

As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in ...


Cytongrasp: Cyton Alpha Controller Via Graspit! Simulation, Nicholas Wayne Overfield Dec 2011

Cytongrasp: Cyton Alpha Controller Via Graspit! Simulation, Nicholas Wayne Overfield

Masters Theses

This thesis addresses an expansion of the control programs for the Cyton Alpha 7D 1G arm. The original control system made use of configurable software which exploited the arm’s seven degrees of freedom and kinematic redundancy to control the arm based on desired behaviors that were configured off-line. The inclusions of the GraspIt! grasp planning simulator and toolkit enables the Cyton Alpha to be used in more proactive on-line grasping problems, as well as, presenting many additional tools for on-line learning applications. In short, GraspIt! expands what is possible with the Cyton Alpha to include many machine learning tools ...