Browsing by Author "Visinsky, Monica L."
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Item Adaptive Fault Detection and Tolerance for Robots(TSI Press, 1994-08-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationIn existing robot fault detection schemes, sensed values of the joint status (position, velocity, etc.) are typically compared against expected or desired values, and if a given threshold is exceeded, a fault is inferred. The thresholds tend to be empirically determined and held constant over a wide range of trajectories. This leads to false alarms when the threshold is too small to counter the error-inducing effects model inaccuracy and to undetected faults when the threshold is too large for the given situation. This paper presents new methods for adaptively choosing fault detection thresholds, subject to sensing and modeling inaccuracies and the changing status of the robot. Our approach chooses optimal thresholds based on a Singular Value Decomposition (SVD) of a specialized error regressor format of the dynamics to minimize the possibility of false alarms or undtected failures. The thresholds vary dynamically with the changing trajectory and configuration of the robot and with the robot's failure status. Examples of the fault detection scheme for a non-planar 3 DOF robot are given.Item A Dynamic Fault Tolerance Framework for Remote Robots(IEEE, 1995-08-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault tolerance is increasingly important for robots, especially those in remote or hazardous environments. Robots need the ability to effectively detect and tolerate internal failures in order to continue performing their tasks without the need for immediate human intervention. This paper presents a layered fault tolerance framework containing new fault detection and tolerance schemes. The framework is divided into servo, interface, and supervisor layers. The servo layer is the continuous robot system and its normal controller. The interface layer monitors the servo layer for sensor or motor failures using analytical redundancy based fault detection tests. A newly developed algorithm generates the dynamic thresholds necessary to adapt the detection tests to the modeling inaccuracies present in robotic control. Depending on the initial conditions, the interface layer can provide some sensor fault tolerance automatically without direction from the supervisor. If the interface runs out of alternatives, the discrete event supervisor searches for remaining tolerance options and initiates the appropriate action based on the current robot structure indicated by the fault tree database. The layers form a hierarchy of fault tolerance which provide different levels of detection and tolerance capabilities for structurally diverse robots.Item Dynamic Senor-Based Fault Detection for Robots(SPIE - The International Society for Optical Engineering, 1993-09-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault detection and fault tolerance are increasingly important for robots in space or hazardous environments due to the dangerous and often inaccessible nature of these environs. We have previously developed algorithms to enable robots to autonomously cope with failures or critical sensors and motors. Typically, the detection thresholds used in such algorithms to mask out model and sensor errors are empirically determined and are based on a specific robot trajectory. We have noted, however, that the effect of model and sensor inaccuracy fluctuates dynamically as the robot and as failures occur. The thresholds, therefore, need to be more dynamic and respond to the changes in the robot system so as to maintain an optimal bound for sensing real failures in the system versus misalignment due to modeling errors. In this paper, we analyze the Reachable Measurement Intervals method of computing dynamic thresholds and explore its applicability to robotic fault detection.Item Expert System Framework for Fault Detection and Fault Tolerance in Robotics(ASME, 1992-11-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault tolerance is of increasing importance for modern autonomous industrial robots. The ability to detect and tolerate failures will enable robots to effectively cope with internal failures and continue performing assigned tasks without the need for immediate human intervention. To monitor fault tolerance actions performed by lower level routines and to provide higher level information about a robot's recovery capabilities, we use an expert system to develop a novel fault tolerance framework combining fault detection and tolerance routines with dynamic fault tree analysis. Fault tree analysis reveals the key components for providing fault detection and tolerance within a system. The trees can also be used quantitatively to provide a dynamic estimate of the probability of failure of the entire system or various subsystems. Using fault trees as a standard framework, the expert system package can provide fault tolerance for robots of significantly different origin and structure.Item Expert System Framework for Fault Detection and Fault Tolerance in Robotics(Elsevier Science Ltd, 1994-01-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault tolerance is of increasing importance for modern robots. The ability to detect and tolerate failures enables robots to effectively cope with internal failures and continue performing assigned tasks without the need for immediate human intervention. To monitor fault tolerance actions performed by lower level routines and to provide higher level information about a robot's recovery capabilities, we present an expert system and critic which together form a novel and intelligent fault tolerance framework integrating fault detection and tolerance routines with dynamic fault tree analysis. A higher level, operating system inspired critic layer provides a buffer between robot fault tolerant operations and the user. The expert system gives the framework the modularity and flexibility to quickly convert between a variety of robot structures and tasks. It also provides a standard interface to the fault detection and tolerance software and a more intelligent means of monitoring the progress of failure and recovery throughout the robot system. The expert system further allows for prioritization of tasks so that the components essential to fault detection and tolerance within a system and detail the interconnection between failures in the system. The trees are also used quantitatively to provide a dynamic estimate of the probability of failure of the entire system or various subsystems.Item Fault Detection and Fault Tolerance in Robotics(1991-07-01) Visinsky, Monica L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia CommunicationRobots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth. Future work will extend the analyses done in this paper to enhance Trick, a robotic simulation testbed, with fault tolerant capabilities in an expert system package.Item Fault Detection and Fault Tolerance Methods for Robotics(1991-12-20) Visinsky, Monica L.; Center for Multimedia Communications (http://cmc.rice.edu/)Fault tolerance is increasingly important in modern autonomous or industrial robots. The ability to detect and tolerate failures allows robots to effectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention. To support these fault tolerant capabilities, methods of detecting and isolating failures must be perfected. This thesis presents new fault detection algorithms which detect failures in robot components using analytical redundancy relations. The robot components critical to fault detection are revealed using an extended fault tree analysis. The thesis validates the algorithms using a simulated robot failure testbed. An intelligent fault tolerance framework is proposed in which a fault tree database and the detection algorithms work together to detect and tolerate sensor or motor failures in a robot system. Future work will expand the detection and tolerance routines and embed the framework into a more exible expert system package.Item Fault Tolerant Algorithms and Architectures for Robotics(IEEE, 1994-04-01) Hamilton, D.L.; Visinsky, Monica L.; Bennett, J.K.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationAs robot tasks in space, nuclear, and medical environments become more widespread, the issues of reliability and safety for robots are becoming more critical. Attempts to address these issues have resulted in a a recent surge of activity in robot fault tolerance. We concentrate on fault tolerance in the robot controller, and highlight the importance and potential of multiprocessor control architectures from the fault tolerance perspective. The issue of performance versus reliability is discussed. This paper also summarizes other work by our group at Rice University in the area of fault tolerance for robotics.Item Layered Dynamic Fault Detection and Tolerance for Robots(IEEE Computer Society Press, 1993-05-01) Visinsky, Monica L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia CommunicationFault tolerance is increasingly important for space and hazardous environment robotics. Robots need to quickly detect and tolerate internal failures in order to continue performing their tasks without the need for immediate human intervention. Using analytical redundancy, this paper derives tests with which the robot can detect failures. The paper also develops a layered intelligent control framework containing these new sensor-based fault detection and tolerance schemes. the servo, interface, and supervisor layers form a hierarchy of fault tolerance which provide different levels of detection and tolerance capabilities for structurally diverse robots.Item New Dynamic Model-Based Fault Detection Thresholds for Robot Manipulators(IEEE Computer Society Press, 1994-05-01) Visinsky, Monica L.; Walker, Ian D.; Cavallaro, Joseph R.; Center for Multimedia CommunicationAutonomous robotic fault detection is becoming increasingly important as robots are used in more inaccessible and hazardous environments. Detection algorithms, however, are adversely effected by the model simplification, parameter uncertainty, and computational inaccuracy inherent in robotic control, leading to an unacceptable number of false alarms and overzealous fault tolerance. The algorithms must use thresholds to mask out these errors. Typically, the thresholds are empirically determined from a specific robot trajectory. The effect of modeling inaccuracy, however, fluctuates dynamically as the robot moves and failures occur. The thresholds need to be dynamic and respond to the changes in the robot system so as to differentiate between real failures and misalignment due to modeling errors. This paper first summarizes the Reachable Measurement Intervals (RMI) method of computing dynamic thresholds and then, learning from the robot-oriented analysis of RMI, presents a more efficient threshold generation method using the manipulator dynamics property of linearity in parameters.Item Robot Fault Detection and Fault Tolerance: A Survey(Elsevier Science Limited, 1994-01-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault tolerance is increasingly important for robots, especially those in remote or hazardous environments. Robots need the ability to effectively detect and tolerate internal failures in order to continue performing their tasks without the need for immediate human intervention. Recently, there has been a surge of interest in robot fault tolerance, and the subject has been investigated from a number of points of view. Ongoing research performs off-line and on-line failure analyses of robotic systems, develops fault-tolerant control environments, and derives fault detection and error recovery techniques using hardware, kinematic, or functional redundancy. This paper presents a summary of the current, limited, state-of-the-art in fault-tolerant robotics and offers some future possibilities for the field.Item Robotic Fault Tolerance: Algorithms and Architectures(Prentice Hall, Englewood Cliffs, NJ, 1993-04-01) Visinsky, Monica L.; Cavallaro, Joseph R.; Walker, Ian D.; Center for Multimedia CommunicationFault tolerance is an essential factor in ensuring successful autonomous systems, especially for robots working in remote or hazardous environments. To avoid the cost and risk involved in sending humans into these environs and improve the chances of mission success, robots must be able to quickly detect and recover from internal failures without relying on immediate repairs or human intervention. In developing robotic fault tolerance algorithms, the possible failures within the robot and the interdependence of these failures must first be determined. One useful tool for performing these tasks is Fault Tree Analysis. The tree structures developed by this technique provide a flow chart of possible robot fault events and define the cause and effect relationships between the failures.