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 11

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
Social Entropy: a New Metric for Learning Multi-Robot Teams

(Georgia Institute of Technology, 1997) Balch, Tucker

As robotics research expands into multiagent tasks and learning, investigators need new tools for evaluating the artificial robot societies they study. Is it enough, for example, just to say a team is "heterogeneous?" Perhaps heterogeneity is more properly viewed on a sliding scale. To address these issues this paper presents new metrics for learning robot teams. The metrics evaluate diversity in societies of mechanically similar but behaviorally heterogeneous agents. Behavior is an especially important dimension of diversity in learning teams since, as they learn, agents choose between hetero- or homogeneity based solely on their behavior. This paper introduces metrics of behavioral difference and behavioral diversity. Behavioral difference refers to disparity between two specific agents, while diversity is a measure of an entire society. Social Entropy, inspired by Shannon's Information Entropy [5], is proposed as a metric of behavioral diversity. It captures important components of diversity including the number and size of castes in a society. The new metrics are illustrated in the evaluation of an example learning robot soccer team.
AuRA: Principles and Practice in Review

(Georgia Institute of Technology, 1997) Arkin, Ronald C. ; Balch, Tucker

This paper reviews key concepts of the Autonomous Robot Architecture {AuRA}. Its structure, strengths, and roots in biology are presented. AuRA is a hybrid deliberative/reactive robotic architecture that has been developed and refined over the past decade. In this article, particular focus is placed on the reactive behavioral component of this hybrid architecture. Various real world robots that have been implemented using this architectural paradigm are discussed, including a case study of a multiagent robotic team that competed and won the 1994 AAAI Mobile Robot Competition.
Io, Ganymede and Callisto - a Multiagent Robot Trash-Collecting Team

(Georgia Institute of Technology, 1995) Balch, Tucker ; Boone, Gary Noel ; Collins, Tom ; Forbes, Harold ; MacKenzie, Douglas Christopher ; Santamaria, Juan Carlos

Georgia Tech won the Office Cleanup Event at the 1994 AAAI Mobile Robot Competition with a multi-robot cooperating team. This paper describes the design and implementation of these reactive trash-collecting robots, including details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal sequencing of behaviors for task completion, and a language for specifying motor schema-based robot behaviors.
Communication in Reactive Multiagent Robotic Systems

(Georgia Institute of Technology, 1994) Balch, Tucker ; Arkin, Ronald C.

Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and effectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three different types of communication for three different tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.
Buzz, An Instantiation of a Schema-Based Reactive Robotic System

(Georgia Institute of Technology, 1993) Arkin, Ronald C. ; Balch, Tucker ; Collins, Thomas Riley ; Henshaw, Andrew M. ; MacKenzie, Douglas Christopher ; Nitz, Elizabeth ; Rodriguez, David ; Ward, Keith Ronald

The Georgia Tech entry to the AAAI Mobile Robot Competition, a schema-based reactive robotic system, is described. New developments are presented including the introduction of two novel behaviors probe and avoid-past, specialized planning and sensing strategies, and a transputer implementation of the reactive control system.