Organizational Unit:
Mobile Robot Laboratory

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Author: MacKenzie, Douglas Christopher × End date: 1999 × Start date: 1991 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Institute for Robotics and Intelligent Machines (IRIM) ×

Publication Search Results

Now showing 1 - 10 of 10
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Evaluating the Usability of Robot Programming Toolsets

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.

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Io, Ganymede and Callisto - a Multiagent Robot Trash-Collecting Team

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.

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Buzz, An Instantiation of a Schema-Based Reactive Robotic System

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.

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Perceptual Support for Ballistic Motion in Docking for a Mobile Robot

1991 , MacKenzie, Douglas Christopher , Arkin, Ronald C.

This paper describes ongoing research into methods to allow a mobile robot to effectively function in a manufacturing environment; specifically, generation of the ballistic motion phase of the docking behavior. This overall docking behavior causes the robot to move to a workstation and park in an appropriate position. The docking behavior consists of two distinct types of motion. Ballistic motion rapidly moves the robot to an area near the dock where recognition of the dock triggers the slower, more accurate orienting motion for the final positioning. The ballistic motion is supported with two simple low-level behaviors: a phototropic (light seeking) behavior and a temporal (motion) detection behavior. The phototropic or temporal activity perceptual strategy maneuvers the vehicle toward the bright light or the abundant motion usually associated with workstations. These vision algorithms have been selected because strict knowledge of the initial position of the dock is not needed and each requires limited computational resources. This system has been implemented and shown to successfully generate ballistic motion in support of docking in typical manufacturing environments.

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Multiagent Mission Specification and Execution

1997 , Arkin, Ronald C. , Cameron, Jonathan M. , MacKenzie, Douglas Christopher

Specifying a reactive behavioral configuration for use by a multiagent team requires both a careful choice of the behavior set and the creation of a temporal chain of behaviors which executes the mission. This difficult task is simplified by applying an object-oriented approach to the design of the mission using a construction called an assemblage and a methodology called temporal sequencing. The assemblage construct allows building high level primitives which provide abstractions for the designer. Assemblages consist of groups of basic behaviors and coordination mechanisms that allow the group to be treated as a new coherent behavior. Upon instantiation, the assemblage is parameterized based on the specific mission requirements. Assemblages can be re-parameterized and used in other states within a mission or archived as high level primitives for use in subsequent projects. Temporal sequencing partitions the mission into discrete operating states with perceptual triggers causing transitions between those states. Several smaller independent configurations (assemblages) can then be created which each implement one state. The Societal Agent theory is presented as a basis for constructions of this form. The Configuration Description Language (CDL) is developed to capture the recursive composition of configurations in an architecture- and robot-independent fashion. The MissionLab system, an implementation based on CDL, supports the graphical construction of configurations using a visual editor. Various multiagent missions are demonstrated in simulation and on our Denning robots using these tools.

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Specification and Execution of Multiagent Missions

1995 , MacKenzie, Douglas Christopher , Arkin, Ronald C. , Cameron, Jonathan M.

Specifying a purely reactive behavioral configuration for use by a multiagent team executing a mission requires both a careful choice of the behavior set and the creation of a temporal chain of behaviors which executes the mission. This difficult task is simplified by applying an object-oriented approach to the design of sequences of behavioral configurations where a methodology called temporal sequencing is used to partition the mission into discrete operating states and enumerate the perceptual triggers which cause transitions between those states. Several smaller independent configurations can then be created with each implementing one state, completing one step in the sequence. When properly constructed, these configurations (assemblages) become high level primitives reusable in subsequent projects, reducing development time. In the multi-vehicle domain being studied for the ARPA Demo II project, assemblages such as travel_to_location and occupy_location consist of groups of basic behaviors associated with coordination mechanisms that allow the group to be treated as a single coherent behavior. For example, travel_to_location consists of move_to_goal, avoid_obstacle, avoid_robot, noise, and stay_in_formation primitive behaviors moderated by a cooperative coordination operator. Upon instantiation, the assemblage is parameterized with a particular formation, goal location, and termination conditions. A mission coordination operator determines which assemblage to activate based upon the mission being executed and the current state of the system. A scenario language has been developed which allows specifying missions as sequences of steps, where each step invokes a particular assemblage. The missions are specified in a structured user-friendly language targeted for groups of cooperating robotic vehicles executing military-style scout missions. Various multiagent missions have been demonstrated in simulation using this system. Deployment on Denning mobile robots demonstrates the utility of this mission execution system, while later deployment on the ARPA Demo II test platforms will ultimately allow comparisons with software developed using other methods.

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Making a Clean Sweep: Behavior-Based Vacuuming

1993 , MacKenzie, Douglas Christopher , Balch, Tucker

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Behavior-Based Mobile Manipulation for Drum Sampling

1996 , Arkin, Ronald C. , MacKenzie, Douglas Christopher

This paper describes an implementation of a behavior-based mobile manipulator capable of autonomously transferring a sample from one drum to a second in unstructured environments. A major contribution of the project was the coherent integration of the arm and base as a cohesive unit, and not just a mobile base with an arm attached. The support for smooth simultaneous operation of all joints on the vehicle facilitated biologically plausible motions, such as arm preshaping. The behavior-based controller used a pseudo-force model, where behaviors add forces and torques to joints and limbs resulting in coordinated motion. The vehicle Jacobian is used to convert the pseudo-forces into joint torques and a pseudo-damping model converts the joint torques into joint velocities. This process allows rapid control of the manipulator without the use of inverse kinematics. A drum sampling task is presented where the vehicle demonstrates how a sample of material could be moved from one drum to another, illustrating the efficacy of the solution.

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Planning to Behave: A Hybrid Deliberative/Reactive Robot Control Architecture for Mobile Manipulation

1994 , Arkin, Ronald C. , MacKenzie, Douglas Christopher

Hybrid architectures provide an effective means for integrating world knowledge with reactive control. This paper describes the motivation behind the architectural decision to hybridize, and presents a case study in mobile manipulation in the context of the Autonomous Robot Architecture (AuRA).

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Integrated Control for Mobile Manipulation for Intelligent Materials Handling

1992 , Arkin, Ronald C. , Arya, S. , Book, Wayne J. , Cameron, Jonathan M. , Gardner, Warren F. , Lawton, Daryl T. , MacKenzie, Douglas Christopher , Ramanathan, V. , Son, C. , Vachtsevanos, George J. , Ward, Keith Ronald

An integrated control system architecture for mobile manipulators is presented. This architecture incorporates a hybrid reactive/hierarchical structure and partitions the task into macro- and micro-manipulation components. Computer vision and other sensor modalities provide the input necessary to cope with materials handling tasks in a partially modeled and dynamic world.

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