Organizational Unit:

Mobile Robot Laboratory

Permanent Link

https://hdl.handle.net/1853/70779

Parent Organization

Organizational Unit

College of Computing

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/1110

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Publication Search Results

Now showing 1 - 10 of 16

Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model

(Georgia Institute of Technology, 2003) Balch, Tucker ; Dellaert, Frank ; Khan, Zia

We describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.
Value-Based Communication Preservation for Mobile Robots

(Georgia Institute of Technology, 2003) Balch, Tucker ; Powers, Matthew

Value-Based Communication Preservation (VBCP) is a behavior-based, computationally efficient approach to maintaining line-of-sight RF communication between members of robot teams in the context of other tasks. The goal of VBCP is, at each time step, to reactively choose a direction in which to move that provides the best communication quality of service with the rest of the team. VBCP uses information about other robots, real-time quality of service measurements and an a priori map of the environment to approximate an optimal direction in an efficient manner. Here, VBCP maintains communication between members of a robotic team while traversing an urban environment in formation. Quantitative and qualitative results are demonstrated in simulation and physical robot teams.
Niche Selection for Foraging Tasks in Multi-Robot Teams Using Reinforcement Learning

(Georgia Institute of Technology, 2003) Balch, Tucker ; Ulam, Patrick D.

We present a means in which individual members of a multi-robot team may allocate themselves into specialist and generalist niches in a multi-foraging task where there may exist a cost for generalist strategies. Through the use of reinforcement learning, we show that the members can allocate themselves into effective distributions consistent with those distributions predicted by optimal foraging theory. These distributions are established without prior knowledge of the environment, without direct communication between team members, and with minimal state.
The Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue

(Georgia Institute of Technology, 2002) Alegre, Fernando ; Balch, Tucker ; Berhault, Marc ; Dellaert, Frank ; Kaess, Michael ; McGuire, Robert ; Merrill, Ernest ; Moshkina, Lilia ; Ravichandran, Ram ; Walker, Daniel

We describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.
Distributed Sensor Fusion for Object Position Estimation by Multi-Robot Systems

(Georgia Institute of Technology, 2001) Balch, Tucker ; Martin, Martin C. ; Stroupe, Ashley W.

We present a method for representing, communicating and fusing distributed, noisy and uncertain observations of an object by multiple robots. The approach relies on re-parameterization of the canonical two-dimensional Gaussian distribution that corresponds more naturally to the observation space of a robot. The approach enables two or more observers to achieve greater effective sensor coverage of the environment and improved accuracy in object position estimation. We demonstrate empirically that, when using our approach, more observers achieve more accurate estimations of an object’s position. The method is tested in three application areas, including object location, object tracking, and ball position estimation for robotic soccer. Quantitative evaluations of the technique in use on mobile robots are provided.
Behavior-based Formation Control for Multi-robot Teams

(Georgia Institute of Technology, 1999) Arkin, Ronald C. ; Balch, Tucker

New reactive behaviors that implement formations in multi-robot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based Unmanned Ground Vehicles. The technique has been integrated with the Autonomous Robot Architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments.
Reward and Diversity in Multirobot Foraging

(Georgia Institute of Technology, 1999) Balch, Tucker

This research seeks to quantify the impact of the choice of reward function on behavioral diversity in learning robot teams. The methodology developed for this work has been applied to multirobot foraging, soccer and cooperative movement. This paper focuses specifically on results in multirobot foraging. In these experiments three types of reward are used with Q-learning to train a multirobot team to forage: a local performance-based reward, a global performance-based reward, and a heuristic strategy referred to as shaped reinforcement. Local strategies provide each agent a specific reward according to its own behavior, while global rewards provide all the agents on the team the same reward simultaneously. Shaped reinforcement provides a heuristic reward for an agent's action given its situation. The experiments indicate that local performance-based rewards and shaped reinforcement generate statistically similar results: they both provide the best performance and the least diversity. Finally, learned policies are demonstrated on a team of Nomadic Technologies' Nomad-150 robots.
Cooperative Multiagent Robotic Systems

(Georgia Institute of Technology, 1997) Arkin, Ronald C. ; Balch, Tucker
Design and Implementation of a Teleautonomous Hummer

(Georgia Institute of Technology, 1997) Ali, Khaled Subhi ; Arkin, Ronald C. ; Balch, Tucker ; Bentivegna, Darrin Charles

Autonomous and semi-autonomous full-sized ground vehicles are becoming increasingly important, particularly in military applications. Here we describe the instrumentation of one such vehicle, a 4-wheel drive Hummer, for autonomous robotic operation. Actuators for steering, brake, and throttle have been implemented on a commercially available Hummer. Control is provided by on-board and remote computation. On-board computation includes a PC-based control computer coupled to feedback sensors for the steering wheel, brake, and forward speed; and a Unix workstation for high-level control. A radio link connects the on-board computers to an operator's remote workstation running the Georgia Tech MissionLab system. The paper describes the design and implementation of this integrated hardware/software system that translates a remote human operator's commands into directed motion of the vehicle. Telerobotic control of the hummer has been demonstrated in outdoor experiments.
Integrating RL and Behavior-Based Control for Soccer

(Georgia Institute of Technology, 1997) Balch, Tucker