Abstract—Techniques for diagnosing faults in hybrid systems that combine digital (discrete) supervisory controllers with analog (continuous) plants need to be different from those used for discrete or continuous systems. This paper presents a methodology for online tracking and diagnosis of hybrid systems. We demonstrate the effectiveness of the approach with experiments conducted on the fuel-transfer system of fighter aircraft. Index Terms—Fault detection and isolation, hybrid systems, model-based diagnosis (MBD). I.

Fault diagnosis is a critical task for autonomous operation of systems such as spacecraft and planetary rovers, and must often be performed on-board.

This paper shows how state-of-the-art state estimation techniques can be used to provide efficient solutions to the difficult problem of real-time diagnosis in mobile robots. The power of the adopted estimation techniques resides in our ability to combine particle filters with classical algorithms, such as Kalman filters. We demonstrate these techniques in two scenarios: a mobile waiter robot and planetary rovers designed by NASA for Mars exploration. Keywords—Diagnosis, Rao–Blackwellized particle filtering, robotics, state estimation. I.

Model-based diagnosis of embedded systems relies on the ability to estimate their hybrid state from noisy observations. This task is especially challenging for systems with many state variables and autonomous transitions. We propose

An increased level of autonomy is critical for meeting many of the goals of advanced planetary rover missions such as NASA's 2009 Mars Science Lab. One important component of this is state estimation, and in particular fault detection on-board the rover. In this paper we describe the results of a project funded by the Mars Technology Program at NASA, aimed at developing algorithms to meet this requirement. We describe a number of particle filtering-based algorithms for state estimation which we have demonstrated successfully on diagnosis problems including the K-9 rover at NASA Ames Research Center and the Hyperion rover at CMU. Because of the close interaction between a rover and its environment, traditional discrete approaches to diagnosis are impractical for this domain. Therefore we model rover subsystems as hybrid discrete/continuous systems. There are three major challenges to make particle filters work in this domain. The first is that fault states typically have a very low probability of occurring, so there is a risk that no samples will enter fault states. The second issue is coping with the high-dimensional continuous state spaces of the hybrid system models, and the third is the severely constrained computational power available on the rover. This means that very few samples can be used if we wish to track the system state in real time. We describe a number of approaches to rover diagnosis specifically designed to address these challenges.

The ability to detect failures and to analyze their causes is one of the preconditions of truly autonomous mobile robots. Especially online failure detection is a complex task, since the effects of failures are typically difficult to model and often resemble the noisy system behavior in a fault-free operational mode. The extremely low a priori likelihood of failures poses additional challenges for detection algorithms. In this paper, we present an approach that applies Gaussian process classification and regression techniques for learning highly effective proposal distributions of a particle filter that is applied to track the state of the system. As a result, the efficiency and robustness of the state estimation process is substantially improved. In practical experiments carried out with a real robot we demonstrate that our system is capable of detecting collisions with unseen obstacles while at the same time estimating the changing point of contact with the obstacle. 1

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Abstract — Fault diagnosis of coupled mobile robots requires a large number of measurements to be communicated either between the robots or from the robots to a central diagnoser. As computational complexity increases with the number of measurements, centralized algorithms become inefficient. This paper presents a distributed approach for qualitative fault diagnosis of coupled mobile robots. The approach is based on a bond graph modeling framework which incorporates local and distributed control algorithms, multiple sensor types, and both actuator and sensor faults. Relative measurement orderings are introduced to discriminate faults by exploiting the temporal order of the measurement deviations. This increases the discriminatory power of a set of measurements and results in a more efficient qualitative diagnosis algorithm. Distributed diagnosers are designed and applied to coupled mobile robots. Experimental results for a system consisting of two robots pushing a box demonstrate the improvement in both discriminatory power of the measurements and efficiency of the distributed diagnosis approach. I.

Abstract—Multirobot systems are being increasingly used for a variety of tasks in manufacturing, surveillance, and space exploration. These systems can degrade or develop faults during operation, and, therefore, require online diagnosis algorithms to ensure safe operation. Centralized approaches to online diagnosis of robot formations do not scale well for two primary reasons: 1) the computational complexity of the algorithm grows significantly with the number of robots, and 2) the individual robots must communicate a large number of measurements to a central diagnoser. To overcome these problems, we present a distributed, model-based, qualitative fault-diagnosis approach for formations of mobile robots. The approach is based on a bond-graph modeling framework that can deal with multiple sensor types and isolate process, sensor, and actuator faults. The diagnosis scheme employs relative measurement orderings to discriminate among faults by exploiting the temporal order of measurement deviations. This increases the discriminatory power of the measurement set and produces a more efficient fault-isolation algorithm. We describe a distributed diagnoser design algorithm applied to robot formations. Experimental results demonstrate the improvement in both the discriminatory power of the measurements produced by the relative measurement orderings, and the computational efficiency achieved by the distributed-diagnosis approach. Index Terms—Mobile robots, model-based diagnosis, multirobot systems. I.

In recent years several approaches have been proposed for model-based diagnosis of hybrid systems. These approaches deal with discrete or parametric faults, and perform consistency-based, stochastic or mixed reasoning. The major restriction that a diagnosis application designer faces is that each technique uses its own modeling paradigm and the reasoning algorithms implement a single strategy. Diagnosis application designers would like to have the flexibility of building models that are suitable for the task (e.g., appropriate abstraction level, appropriate details, type of modeling paradigm used). They would also like to have the flexibility in choosing the strategies used in the diagnostic reasoning process. We propose a general framework for stochastic and hybrid model-based diagnosis called Hybrid Diagnosis Engine (HyDE) that offers this flexibility to the diagnosis application designer. We list the key steps in stochastic and hybrid diagnosis and identify the kinds of models that are needed in those steps. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. We discuss the current status of HyDE and its application in diagnosis of real and conceptual systems.