Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

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https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Now showing 1 - 10 of 32

Autobed: A Web-Controlled Robotic Bed

(Georgia Institute of Technology, 2016-02) Grice, Phillip M. ; Chitalia, Yash ; Rich, Megan ; Clever, Henry ; Evans, Henry ; Evans, Jane ; Kemp, Charles C.

We (the Healthcare Robotics Lab at Georgia Tech) have developed an additional module for an Invacare fully electric hospital bed (Model 5410IVC) so that the bed can be controlled from a web-based interface. This module can be easily plugged between the hand control and the Invacare bed, without having to modify any existing hardware on the bed. We call a bed so modified an 'Autobed.' With this feature, users who are unable to operate the standard bed controls, but can access a web browser, are able to position the bed by themselves without having to rely on a caregiver (for example, patients with quadriplegia). This page describes how to make the Autobed module using relatively inexpensive, commercially available hardware. This document is a representation of the content provided at http://hsi.gatech.edu/hrl/project_autobed_v2.shtml as of February 15th, 2016, and is intended to create a lasting, citable, and archival copy of this material, which details the design and instructions for building the 'Autobed' device.
Supplementary Material to: IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

(Georgia Institute of Technology, 2015-05-30) Forster, Christian ; Carlone, Luca ; Dellaert, Frank ; Scaramuzza, Davide
Goal Reasoning: Papers from the ACS Workshop

(Georgia Institute of Technology, 2015-05-28) Aha, David W. ; Anderson, Tory S. ; Bengfort, Benjamin ; Burstein, Mark ; Cerys, Dan ; Coman, Alexandra ; Cox, Michael T. ; Dannenhauer, Dustin ; Floyd, Michael W. ; Gillespie, Kellen ; Goel, Ashok K. ; Goldman, Robert P. ; Jhala, Arnav ; Kuter, Ugur ; Leece, Michael ; Maher, Mary Lou ; Martie, Lee ; Merrick, Kathryn ; Molineaux, Matthew ; Muñoz-Avila, Héctor ; Roberts, Mark ; Robertson, Paul ; Rugaber, Spencer ; Samsonovich, Alexei ; Vattam, Swaroop S. ; Wang, Bing ; Wilson, Mark

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013.
The Effect of Robot Performance on Human-‐Robot Trust in Time-‐Critical Situations

( 2015-01) Robinette, Paul ; Wagner, Alan R. ; Howard, Ayanna M.

We vary the ability of robots to mitigate a participant’s risk in a navigation guidance task to determine the effect this has on the participant’s trust in the robot in a second round. A significant loss of trust was found after a single robot failure.
Analytic Inverse Kinematics for the Universal Robots UR-5/UR-10 Arms

(Georgia Institute of Technology, 2013-12-07) Hawkins, Kelsey P.
Rapid Loop Updates

(Georgia Institute of Technology, 2012-09-11) Indelman, Vadim ; Dellaert, Frank
Factor Graphs and GTSAM: A Hands-on Introduction

(Georgia Institute of Technology, 2012-09) Dellaert, Frank

In this document I provide a hands-on introduction to both factor graphs and GTSAM. Factor graphs are graphical models (Koller and Friedman, 2009) that are well suited to modeling complex estimation problems, such as Simultaneous Localization and Mapping (SLAM) or Structure from Motion (SFM). You might be familiar with another often used graphical model, Bayes networks, which are directed acyclic graphs. A factor graph, however, is a bipartite graph consisting of factors connected to variables. The variables represent the unknown random variables in the estimation problem, whereas the factors represent probabilistic information on those variables, derived from measurements or prior knowledge. In the following sections I will show many examples from both robotics and vision. The GTSAM toolbox (GTSAM stands for “Georgia Tech Smoothing and Mapping”) toolbox is a BSD-licensed C++ library based on factor graphs, developed at the Georgia Institute of Technology by myself, many of my students, and collaborators. It provides state of the art solutions to the SLAM and SFM problems, but can also be used to model and solve both simpler and more complex estimation problems. It also provides a MATLAB interface which allows for rapid prototype development, visualization, and user interaction. GTSAM exploits sparsity to be computationally efficient. Typically measurements only provide information on the relationship between a handful of variables, and hence the resulting factor graph will be sparsely connected. This is exploited by the algorithms implemented in GTSAM to reduce computational complexity. Even when graphs are too dense to be handled efficiently by direct methods, GTSAM provides iterative methods that are quite efficient regardless. You can download the latest version of GTSAM at http://tinyurl.com/gtsam.
Robots that Need to Mislead: Biologically-inspired Machine Deception

(Georgia Institute of Technology, 2012) Arkin, Ronald C.
Mobile Manipulation in Domestic Environments Using A Low Degree of Freedom Manipulator

(Georgia Institute of Technology, 2012) Huckaby, Jacob ; Nieto-Granda, Carlos ; Rogers, John G. ; Trevor, Alexander J. B. ; Cosgun, Akansel ; Christensen, Henrik I.

We present a mobile manipulation system used by the Georgia Tech team in the RoboCup@Home 2010 competition. An overview of the system is provided, including the approach taken for manipulation, SLAM, object detection, object recognition, and system integration. We focus on our manipulation strategy, which utilizes a low-degree of freedom manipulator and makes use of the robot’s differential drive as part of the manipulation strategy. Empirical results demonstrating our platform’s ability to detect and grasp a variety of tabletop objects are presented.
Mobbing Behavior and Deceit and its role in Bioinspired Autonomous Robotic Agents

(Georgia Institute of Technology, 2012) Davis, Justin ; Arkin, Ronald C.

Arabian babblers are highly preyed upon avians living in the Israeli desert. The survival of this species is contingent upon successful predator deterrence known as mobbing. Their ability to successfully defend against larger predators is the inspiration for this research with the goal of employing new models of robotic deception. Using Grafen's Dishonesty Model [3], simulation results are presented, which portend the value of this behavior in military situations.