Showing papers on "Autonomous system (mathematics) published in 1991"

Blanche-an experiment in guidance and navigation of an autonomous robot vehicle

Abstract: The principal components and capabilities of Blanche, an autonomous robot vehicle, are described. Blanche is designed for use in structured office or factory environments rather than unstructured natural environments, and it is assumed that an offline path planner provides the vehicle with a series of collision-free maneuvers, consisting of line and arc segments, to move the vehicle to a desired position. These segments are sent to a low-level trajectory generator and closed-loop motion control. The controller assumes accurate knowledge of the vehicle's position. Blanche's position estimation system consists of a priori map of its environment and a robust matching algorithm. The matching algorithm also estimates the precision of the corresponding match/correction that is then optimally (in a maximum-likelihood sense) combined with the current odometric position to provide an improved estimate of the vehicle's position. The system does not use passive or active beacons. Experimental results are reported. >

Model-based task planning system for a space laboratory environment

Abstract: This paper describes the design of a model-based autonomous planning system that will enable robots to manage a space-borne chemical laboratory. In a model-based planning system, knowledge is encapsulated in the form of models at the various layers to support the predefined system objectives. Thus the model-based approach can be considered as an extended planning paradigm which is able to base its planning, control, diagnosis, repair, and other activities on a variety of objectives-related models. A System Entity Structure/Model Base framework is employed to support autonomous system design through the ability to generate a family of planning alternatives as well as to build hierarchical event-based control structures. The model base is a multilevel, multiabstraction, and multiformalism system organized through the use of system morphisms to integrate related models.

Learning Control for Autonomous Machines

Abstract: Today's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.

Decoupling in nonlinear singular systems with applications in autonomous vehicles

Abstract: In this paper the problem of input-output decoupling in nonlinear singular systems is solved via the application of a particular nonlinear PD feedback control law. An invertible transformation is provided, which converts the closed loop singular system into an equivalent regular, to which a static state feedback law is applied. This transformation, hence, allows the theory that exists for the control of regular nonlinear systems to be applied in the nonlinear singular systems. At the end an application of the theory is tested on a nonlinear singular model of an autonomous excavator.

Imaging And Controls For Mars Robots With Neural Networks

Abstract: Two aspects of the design of space robots is covered implemented by neural networks and by hybrid approach with artificial intelligence. One is a neurocontroller for a real-time autonomous system. An optical control system developed saves the time for the image processing that analyzes an image sensor through the environment and induces a transformation over the sensor array. A prototype of the neurocontroller is able to learn and control by itself. The second aspect deals with the design of a Servo Control System for a Robot with the capability of "learning in Unanticipated Situations" incorporated in the system. The robot is assumed to be employed to perform useful tasks in an alien evironment. The model developed is shown to provide the robot with the capability to recover from unanticipated situations that can lead to the disruption of its normal operation, and to learn to avoid such situations in the future. These two aspects will be integrated for a design of a very intelligent autonomous space robot.