Fault Detection and Fault Tolerance in Robotics

Robots 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.National Science FoundationMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

New Dynamic Model-Based Fault Detection Thresholds for Robot Manipulators

Autonomous 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.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

Robotic Fault Tolerance: Algorithms and Architectures

Fault 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

Dynamic Senor-Based Fault Detection for Robots

Fault 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.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

Layered Dynamic Fault Detection and Tolerance for Robots

Fault 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.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

Adaptive Fault Detection and Tolerance for Robots

In 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.National Science FoundationSandia National LaboratoryMitre Corporation Graduate FellowshipNSF Graduate Fellowshi

A Dynamic Fault Tolerance Framework for Remote Robots

Fault 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

Fault Tolerant Algorithms and Architectures for Robotics

As 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.National Science FoundationSandia National LaboratoryNASA Graduate FellowshipNSF Graduate Fellowshi