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Adaptive Multi-Robot Behavior via Learning Momentum

Submitted by Trex on Thu, 06/05/2008 - 10:55.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
J. Brian Lee, Ronald C. Arkin
Description

A Reactive Robot Architecture with Planning on Demand

Submitted by Trex on Thu, 06/05/2008 - 10:57.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Ananth Ranganathan, Sven Koenig
Description In this paper, we describe a reactive robot architecture that uses fast re-planning methods to avoid the shortcomings of reactive navigation, such as getting stuck in box canyons or in front of small openings. Our robot architecture differs from others in that it gives planning progressively greater control of the robot if reactive navigation continues to fail, until planning controls the robot directly.

Anticipatory Robot Navigation by Simultaneously Localizing and Building a Cognitive Map

Submitted by Trex on Thu, 06/05/2008 - 10:58.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Yoichiro Endo, Ronald C. Arkin
Description This paper presents a method for a mobile robot to construct and localize relative to a “cognitive map”, where the cognitive map is assumed to be a representational structure that encodes both spatial and behavioral

Internalized Plans for Communication-Sensitive Robot Team Behaviors

Submitted by Trex on Thu, 06/05/2008 - 11:00.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Alan R.Wagner, Ronald C. Arkin
Description

Proprioceptive Control for a Robotic Vehicle Over Geometric Obstacles

Submitted by Trex on Thu, 06/05/2008 - 11:02.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Kenneth J. Waldron, Ronald C. Arkin, Douglas Bakkum, Ernest Merrill , Muhammad Abdallah
Description

In this paper we describe a software system built to coordinate an autonomous vehicle with variable configuration ability operating in rough terrain conditions. The paper describes the system architecture, with an emphasis on the action planning function. This is intended to work with a proprioceptive algorithm that continuously coordinates wheel torques and suspension forces and positions to achieve optimal terrain crossing performance.

Compact Encoding of Robot-Generated 3D Maps for Effcient Wireless Transmission

Submitted by Trex on Thu, 06/05/2008 - 11:03.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Michael Kaess, Ronald C. Arkin, Jarek Rossignac
Description

MULTISTRATEGY LEARNING METHODS FOR MULTIROBOT SYSTEMS

Submitted by Trex on Thu, 06/05/2008 - 11:05.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
R.C. Arkin, Y. Endo, B. Lee, D. MacKenzie, E. Martinson
Description

This article describes three different methods for introducing machine learning into a hybrid deliberative/reactive architecture for multirobot systems: learning momentum, Q-learning, and CBR wizards. A range of simulation experiments and results are reported using the Georgia Tech MissionLab mission specification system.

Learning to Role-Switch in Multi-Robot Systems

Submitted by Trex on Thu, 06/05/2008 - 11:07.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Eric Martinson, Ronald C. Arkin
Description We present an approach that uses Q-learning on individual robotic agents, for coordinating a missiontasked team of robots in a complex scenario.

The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue

Submitted by Trex on Thu, 06/05/2008 - 11:13.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Frank Dellaert, Tucker Balch, Michael Kaess, Ram Ravichandran, Fernando Alegre, Marc Berhault, Robert McGuire,Ernest Merrill, Lilia Moshkina,Daniel Walker
Description

Linear 2D Localization and Mapping for Single and Multiple Robot Scenarios

Submitted by Trex on Thu, 06/05/2008 - 11:15.
in
File Type: 
Document
Document Type: 
Best Practices
 Organization:
Georgia Institute of Technology
Author: 
Frank Dellaert, Ashley W. Stroupe
Description We show how to recover 2D structure and motion linearly in order to initialize Simultaneous Mapping and Localization (SLAM) for bearings-only measurements and planar motion. The method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Hence, it substantially enlarges the scope in which non-linear batchtype SLAM algorithms can be applied.
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