Organizational Unit:
Mobile Robot Laboratory
Mobile Robot Laboratory
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Organizational Unit
Includes Organization(s)
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 7 of 7
-
ItemThe Coordination of Deliberative Reasoning in a Mobile Robot(Georgia Institute of Technology, 2009) Ulam, Patrick D.This paper examines the problem of how a mobile robot may coordinate among multiple, possibly conflicting deliberative processes for reasoning about object interactions in a soccer domain. This paper frames deliberative coordination as an instance of the algorithm selection problem and describes a novel framework by which a mobile robot may learn to coordinate its deliberative reasoning in response to constraints upon processing as well as the performance of each deliberative reasoner. Results of the framework are described for a simulated soccer task in which the robot must predict the motion of a fast moving ball in order to prevent it from reaching the goal area.
-
ItemLek Behavior as a Model for Multi-Robot Systems(Georgia Institute of Technology, 2009-01-01) Duncan, Brittany A. ; Ulam, Patrick D. ; Arkin, Ronald C.Lek behavior is a biological mechanism used by male birds to attract mates by forming a group. This project explores the use of a biological behavior found in many species of birds to form leks to guide the creation of groups of robots. The lek behavior provides a sound basis for multi-robot formation because it demonstrates a group of individual entities forming up around a scarce resource. This behavior can be useful to robots in many situations, with an example scenario the case in which robots were dropped via parachute into an area and then needed to form meaningful task-oriented groups.
-
ItemIntegrated Mission Specification and Task Allocation for Robot Teams - Design and Implementation(Georgia Institute of Technology, 2006) Arkin, Ronald C. ; Endo, Yoichiro ; Ulam, Patrick D. ; Wagner, AlanAs the capabilities, range of missions, and the size of robot teams increase, the ability for a human operator to account for all the factors in these complex scenarios can become exceedingly difficult. Our previous research has studied the use of case-based reasoning (CBR) tools to assist a user in the generation of multi-robot missions. These tools, however, typically assume that the robots available for the mission are of the same type (i.e., homogeneous). We loosen this assumption through the integration of contract-net protocol (CNP) based task allocation coupled with a CBR-based mission specification wizard. Two alternative designs are explored for combining case-based mission specification and CNP-based team allocation as well as the tradeoffs that result from the selection of one of these approaches over the other.
-
ItemMulti-Robot User Interface Modeling(Georgia Institute of Technology, 2006) Wagner, Alan R. ; Endo, Yoichiro ; Ulam, Patrick D. ; Arkin, Ronald C.This paper investigates the problem of user interface design and evaluation for autonomous teams of heterogeneous mobile robots. We explore an operator modeling approach to multi-robot user interface evaluation. Specifically the authors generated GOMS models, a type of user model, to investigate potential interface problems and to guide the interface development process. Results indicate that our interface design changes improve the usability of multi-robot mission generation substantially. We conclude that modeling techniques such as GOMS can play an important role in robotic interface development. Moreover, this research indicates that these techniques can be performed in an inexpensive and timely manner, potentially reducing the need for costly and demanding usability studies.
-
ItemIntegrated Mission Specification and Task Allocation for Robot Teams - Part 2: Testing and Evaluation(Georgia Institute of Technology, 2006) Arkin, Ronald C. ; Endo, Yoichiro ; Ulam, Patrick D. ; Wagner, AlanThis work presents the evaluation of two mission specification and task allocation architectures. These architectures, described in part 1 of this paper, present novel means with which to integrate a case-based reasoning (CBR) mission planner with contract net protocol (CNP) based task allocation. In the first design, the CBR and runtime-CNP architecture, the case-based mission planner generates mission plans that support necessary behaviors for CNP-based task allocation and execution. In the second design, the CBR and premission-CNP architecture, task allocation takes place during mission specification. The results of an empirical evaluation of the CBR and runtime-CNP across three naval scenarios is described. Finally, we briefly describe an earlier usability evaluation of the CBR and premission-CNP architecture using goals, operators, methods, and selection rules modeling.
-
ItemWhen Good Comms Go Bad: Communications Recovery for Multi-Robot Teams(Georgia Institute of Technology, 2003) Arkin, Ronald C. ; Ulam, Patrick D.Ad-hoc networks among groups of autonomous mobile robots are becoming a common occurrence as teams of robots take on increasingly complicated missions over wider areas. Research has often focused on proactive means in which the individual robots of the team may prevent communication failures between nodes in this network. This is not always possible especially in unknown or hostile environments. This research addresses reactive aspects of communication recovery. How should the members of the team react in the event of unseen communication failures between some or all of the nodes in the network? We present a number of behaviors to be utilized in the event of communications failure as well as a behavioral sequencer to further enhance the effectiveness of these recovery behaviors. The performance of the communication recovery behaviors is analyzed in simulation and their application on hardware platforms is discussed.
-
ItemNiche 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.