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 48

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.
Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization

(Georgia Institute of Technology, 1999) Burgard, Wolfram ; Dellaert, Frank ; Fox, Dieter ; Thrun, Sebastian

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot’s position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.
Monte Carlo Localization for Mobile Robots

(Georgia Institute of Technology, 1999) Burgard, Wolfram ; Dellaert, Frank ; Fox, Dieter ; Thrun, Sebastian

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular, the problems encountered are closely related to the type of representation used to represent probability densities over the robot’s state space. Recent work on Bayesian filtering with particle-based density representations opens up a new approach for mobile robot localization, based on these principles. In this paper we introduce the Monte Carlo Localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods.
Behavioral Models of the Praying Mantis as a Basis for Robotic Behavior

(Georgia Institute of Technology, 1999) Ali, Khaled Subhi ; Arkin, Ronald C. ; Cervantes-Pérez, Francisco ; Weitzenfeld, Alfredo

Formal models of animal sensorimotor behavior can provide effective methods for generating robotic intelligence. In this article we describe how schema-theoretic models of the praying mantis derived from behavioral and neuroscientific data can be implemented on a hexapod robot equipped with a real-time color vision system. This implementation incorporates a wide range of behaviors, including obstacle avoidance, prey acquisition, predator avoidance, mating, and chantlitaxia behaviors that can provide guidance to neuroscientists, ethologists, and roboticists alike. The goals of this study are threefold: to provide an understanding and means by which fielded robotic systems are not competing with other agents that are more effective at their designated task; to permit them to be successful competitors within the ecological system and capable of displacing less efficient agents; and that they are ecologically sensitive so that agent-environment dynamics are well-modeled and as predictable as possible whenever new robotic technology is introduced.
Implementing Schema-Theoretic Models of Animal Behavior in Robotic Systems

(Georgia Institute of Technology, 1998) Ali, Khaled Subhi ; Arkin, Ronald C.

Formal models of animal sensorimotor behavior can provide effective methods for generating robotic intelligence. In this paper we describe how schema-theoretic models of the praying mantis are implemented on a hexapod robot equipped with a real-time color vision system. The model upon which the implementation is based was developed by ethologists studying mantids. This implementation incorporates a wide range of behaviors, including obstacle avoidance, prey acquisition, predator avoidance, mating, and chantlitaxia behaviors.
A Neural Schema Architecture for Autonomous Robots

(Georgia Institute of Technology, 1998) Arkin, Ronald C. ; Cervantes-Pérez, Francisco ; Corbacho, Fernando ; Olivares, Roberto ; Weitzenfeld, Alfredo

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cognitive Psychology, with applications in Distributed Artificial Intelligence (DAI), Autonomous Agents and Robotics.
Visualization of Multi-Level Neural-Based Robotic Systems

(Georgia Institute of Technology, 1998) Arkin, Ronald C. ; Cervantes-Pérez, Francisco ; Peniche, José Francisco ; Weitzenfeld, Alfredo

Autonomous biological systems are very complex in their nature. Their study, through both experimentation and computation, provides a means to understand the underlying mechanisms in living systems while inspiring the development of technological applications. Experimentation, consisting of data gathering, generates predictions to be validated by experimentation on artificial systems. Computational models provide the understanding for the underlying dynamics, and serve as basis for simulation and further experimentation. The work presented here involves analyzing how predictive models can be generated from biological systems and then be used to drive robotic experiments; and conversely, how can results from robotic experiments drive additional neuroethological data gathering. This process requires a variety of visualization techniques in modeling and simulation of increasingly complex systems.
Evaluating the Usability of Robot Programming Toolsets

(Georgia Institute of Technology, 1997-10-14) Arkin, Ronald C. ; MacKenzie, Douglas Christopher

The days of specifying missions for mobile robots using traditional programming languages such as C++ and LISP are coming to an end. The need to support operators lacking programming skills coupled with the increasing diversity of robot run-time operating systems is moving the field towards high-level robot programming toolsets which allow graphical mission specification. This paper explores the issues of evaluating such toolsets as to their usability. This article first examines how usability criteria are established and performance target values chosen. The methods by which suitable experiments are created to gather data relevant to the usability criteria are then presented. Finally, methods to analyze the data gathered to establish values for the usability criteria are discussed. The MissionLab toolset is used as a concrete example throughout the article to ground the discussions, but the methods and techniques are generalizable to many such systems.
Cooperative Multiagent Robotic Systems

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