Task planning and scheduling techniques developed as part of a project whose goal is to control the operations of many robots in the same environment are presented. Centralized approaches allow task allocation to be i...
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Task planning and scheduling techniques developed as part of a project whose goal is to control the operations of many robots in the same environment are presented. Centralized approaches allow task allocation to be improved. However, they are not appropriate for coordinating the actions of multiple mobile robots in a dynamic environment where unforeseeable events occur. Consequently, the author advocates a mixed strategy, allowing robots to make and execute individual plans as well as to connect with a central task planner and scheduler when appropriate. The author first presents individual planning techniques. Then he describes a framework allowing robots to exchange information with a central system able to optimize task allocation.< >
Most natural and artificial systems rely heavily on vision to recognize, manipulate, and navigate within a world of objects. Although shape is a key element in this process, its representation and analysis have proved...
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Most natural and artificial systems rely heavily on vision to recognize, manipulate, and navigate within a world of objects. Although shape is a key element in this process, its representation and analysis have proved to be a difficult, multifaceted problem. A framework, based on conservation laws, which gives rise to computational elements for shape parts, protrusions, and bends, is proposed. The computation takes place in the context of a reaction-diffusion space and is highly robust. This scheme is ideally suited to object recognition and has applications in areas ranging from robotics to the psychology and the physiology of form.< >
作者：
D.M. LyonsPhilips Laboratories
Autonomous Systems Department North American Philips Corporation Briarcliff Manor NY USA
A process-based model of robot task plan representation is introduced, and some of its advantages are discussed. By process-based it is meant that the plan is represented as a set of concurrent communicating computati...
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A process-based model of robot task plan representation is introduced, and some of its advantages are discussed. By process-based it is meant that the plan is represented as a set of concurrent communicating computational processes, rather than as a graph or a set of local propositions. A description is given of how the authors have developed process composition operators that allow them to represent explicitly and succinctly the relationship between plan components to express sensing and reaction, dynamic resource allocation, and error monitoring and recovery. The approach has formal benefits as well as representation benefits; using the techniques of the (extensive) process algebra literature, they introduce a way to analyze plans (manually or automatically) for liveness and efficiency properties and indicate how the method could help in plan generation.< >
A program of research in robotics that seeks to encode abstract tasks in a form that simultaneously affords a control scheme for the torque-actuated dynamical systems, as well as a proof that the resulting closed-loop...
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A program of research in robotics that seeks to encode abstract tasks in a form that simultaneously affords a control scheme for the torque-actuated dynamical systems, as well as a proof that the resulting closed-loop behavior will correctly achieve the desired goals, is reviewed. Two different behaviors that require dexterity and might plausibly connote 'intelligence'-navigating in a cluttered environment and juggling a number of otherwise freely falling objects-are examined with regard to similarities in problem representation, method of solution, and causes of success. The central theme concerns the virtue of global stability mechanisms. At the planning level they lend autonomy, that is, freedom from dependence upon some 'higher' intelligence. They encourage the design of canonical procedures for model problems, which may then be instantiated in particular settings by a change of coordinates. The procedures developed result in provably autonomous behavior. Simulation results and physical experimental studies suggest the practicability of these methods.< >
The architecture and working of a flexible multiple mobile robot system (MMRS) are described. The use of a path network to define allowed paths for the mobile robots is proposed. This greatly simplifies the detection ...
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The architecture and working of a flexible multiple mobile robot system (MMRS) are described. The use of a path network to define allowed paths for the mobile robots is proposed. This greatly simplifies the detection of possible collisions between the robots and the problem of replanning the motion. The MMRS is divided into three main components standard pattern, central controller, and individual mobile robot. The architecture of the proposed MMRS is presented and its operation discussed in detail. Efficient control algorithms for both the central controller and individual mobile robots are developed and presented. A CAD/simulation system for the design and evaluation of the MMRS is developed. Simulation results to emphasize the validity of the system are presented.< >
A general statistical fusion method motivated by the geometry of uncertainties is proposed for robotic systems with multiple sensors. The treatment of nonlinearity is generalized so as to include both the structural n...
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ISBN:
(纸本)0818619384
A general statistical fusion method motivated by the geometry of uncertainties is proposed for robotic systems with multiple sensors. The treatment of nonlinearity is generalized so as to include both the structural nonlinearity and the computational nonlinearity. First, assuming Gaussian noise additive to the sensory data, the uncertainty ellipsoid associated with the covariance matrix of the error of the sensory information is defined. Second, the optimal fusion is defined as the one, among all the possible linear combinations of sensory information, that minimizes the geometrical volume of the ellipsoid. The resultant fusion equation coincides with those obtained by Bayesian inference, Kalman filter theory, and the weighted least-squares estimation. Finally, the method is extended to include the fusion of partial information.
The authors consider the problem of task-directed information gathering. They first develop a decision-theoretic model of task-directed sensing. In this framework, sensors are modeled as noise-contaminated, uncertain ...
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ISBN:
(纸本)0818619384
The authors consider the problem of task-directed information gathering. They first develop a decision-theoretic model of task-directed sensing. In this framework, sensors are modeled as noise-contaminated, uncertain measurement systems. A a sensor task consists of a function describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system. From this description, the authors develop a computational method approximating a standard Bayesian decision-making model. This algorithm, which relies on a finite-element computation, is applicable to a wide variety of sensor fusion problems. The authors describe its derivation, analyze its error properties, and indicate how it can be made robust to errors in the description of sensors and discrepancies between geometric models and sensed obects. They also present the result of applying this fusion technique to several different information gathering tasks in simulated situations and in a distributed sensing system.
An approach to identifying the kinematic models of manipulators and their task geometry is presented. Starting with the observation that in many tasks manipulators naturally form mobile closed kinematic chains, it is ...
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An approach to identifying the kinematic models of manipulators and their task geometry is presented. Starting with the observation that in many tasks manipulators naturally form mobile closed kinematic chains, it is shown that these closed loops can be identified by an iterative least-squares algorithm similar to that used in calibrating open chain manipulators. By merely using joint angle readings and self motions, consistency conditions can be utilized to identify the kinematic parameters. While the task of a robot opening a door is studied in detail, the method readily generalizes to a large class of robot tasks. Simulations are presented to accompany the analysis.< >
The authors attempt to bridge the planning gap problem that exists between the AI (artificial intelligence) and robotics communities. the objective was determining how procedural and parameter knowledge could be repre...
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The authors attempt to bridge the planning gap problem that exists between the AI (artificial intelligence) and robotics communities. the objective was determining how procedural and parameter knowledge could be represented and used in task planning. Because AI planners lack the ability to deal with kinematic and kinetic information of a world model, and robot planners possess poor reasoning ability, an advanced robotics research environment was considered the appropriate demonstrator. Experiments were conducted on two generic operations that are common to robotics work, grasping and pushing an object, working from a starting point that was randomly selected. Task parameter constraints are derived from rules induced from the small amounts of raw sensory data collected. These rules are then used to indicate whether a task, such as object grasp or push, could be successfully completed.< >
The use of an associative content-addressable memory to model a robot and the world the robot interacts with is discussed. The model can be learned by storing experiences in the memory. To make predictions, the memory...
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The use of an associative content-addressable memory to model a robot and the world the robot interacts with is discussed. The model can be learned by storing experiences in the memory. To make predictions, the memory is searched for relevant experience. An initial implementation of such a memory-based modeling scheme has been made on a parallel computer, the Connection Machine. The implementation machine was used to model and control a simulated planar two-joint arm and a simulated running machine. The issues and problems that arose in the preliminary work are described. It is found that the use of parallel search in the implementation of an associative content-addressable memory allows quick searching of stored experiences, and reasonable retrieval is obtained using a simple distance metric and a simple generalized scheme. The memory is able to generalize after storing only a small number of relevant experiences. The use of search by parallel processors also avoids many of the problems of previous memory-based or tubular approaches to robot modeling (such as search speed and memory requirements).< >
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