Douglas C. MacKenzie

Multiagent Mission Specification and Execution

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.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Temporal coordination of perceptual algorithms for mobile robot navigation

A methodology for integrating multiple perceptual algorithms within a reactive robotic control system is presented. A model using finite state accepters is developed as a means for expressing perceptual processing over space and time in the context of a particular motor behavior. This model can be utilized for a wide range of perceptual sequencing problems. The feasibility of this method is demonstrated in two separate implementations. The first is in the context of mobile robot docking where the mobile robot uses four different vision and ultrasonic algorithms to position itself relative to a docking workstation over a long-range course. The second uses vision, IR beacon, and ultrasonic algorithms to park the robot next to a desired plastic pole randomly placed within an arena. >

Reactive control for mobile manipulation

Research for executing large-scale motions of mobile manipulators is described. Mobile manipulators are mobile bases with an attached arm which function in an integrated manner. Motivation is given for moving the arm while the base is moving. This paper applies reactive control concepts to achieve this type of motion. Tools for modeling integrated arm-vehicle kinematics and dynamics are discussed. Kinematics- and dynamics-based concepts are combined with reactive control approaches in order to move toward a practical level of complexity and performance. Simulation results are presented.<<ETX>>

Evaluating the Usability of Robot Programming Toolsets

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 toward high-level robot programming toolsets that allow graphical mission specification. This paper ex plores the issues of evaluating such toolsets as to their usability. We first examine how usability criteria are established and perfor mance target values are 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.

Specification and execution of multiagent missions

Specifying a multiagent behavioral configuration requires both a careful choice of the behavior set and creation of a temporal chain executing the mission using those behaviors. This difficult task is simplified by applying an object-oriented approach to the design using a methodology called temporal sequencing to partition the mission into discrete operating states and enumerate the perceptual triggers causing transitions between those states. Several smaller independent configurations (assemblages) can then be created, each implementing one distinct operating state. Each assemblage consists of a collection of behaviors and a suitable coordination mechanism which causes the group to act as a single, coherent, behavior. The missions are specified in a structured user-friendly language targeted for military-style scout missions. Various multiagent missions have been demonstrated in simulation and results are shown using our Denning mobile robots.

Behavior-based mobile manipulation for drum sampling

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.

ACTIVE AVOIDANCE: ESCAPE AND DODGING BEHAVIORS FOR REACTIVE CONTROL

New methods for producing avoidance behavior among moving obstacles within the context of reactive robotic control are described. These specifically include escape and dodging behaviors. Dodging is concerned with the avoidance of a ballistic projectile while escape is more useful within the context of chase. The motivation and formulation of these new reactive behaviors are presented. Both simulation and experimental results using a robot in a cluttered and moving world are provided.

Formal specification for behavior-based mobile robots

This paper presents formalisms for describing societies of cooperating behavior-based mobile robots, including the coordination between members of homogeneous teams, members of heterogeneous castes, assemblages of behaviors on individual robots, as well as perceptual strategies within primitive sensorimotor behaviors. This formal language is intended to facilitate proving properties about systems described in it.

Control-driven mapping and planning

Layered hybrid controllers typically include a planner at the top level with reactive control at the lower levels. The planner considers the state of the robot in a global context. The low-level controllers consider only the local environment of the robot and are able to operate at a high frequency to ensure the safety of the robot. Also, it is often the case that the low-level controllers consider more aspects of the robots state (e.g. kinematic constraints) than the planner. The consideration of such constraints at the planning level would prohibitively increase the state space the planner must consider and, accordingly, its running time and complexity. In this paper, we investigate how we can take advantage at the planning level of domain knowledge encapsulated in the lower level controllers, and we introduce a feedback mechanism that enables low-level controllers to influence the high-level planner.