Robotic fault detection using nonlinear analytical redundancy

Leuschen, Martin L.; Cavallaro, Joseph R.; Walker, Ian D.

doi:10.1109/robot.2002.1013402

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…It exploits the available information redundancy in the system (Garcia et al 2000;Jeppesen and Cebon 2004;Leuschen et al 2002). FDI techniques can be broadly classified into: model based and knowledge based models.…”

Section: Fault Tolerancementioning

confidence: 99%

Self-Sufficiency of an Autonomous Reconfigurable Modular Robotic Organism

Qadir¹

2015

Adaptation, Learning, and Optimization

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The role of adaptation, learning and optimization are becoming increasingly essential and intertwined. The capability of a system to adapt either through modification of its physiological structure or via some revalidation process of internal mechanisms that directly dictate the response or behavior is crucial in many real world applications. Optimization lies at the heart of most machine learning approaches while learning and optimization are two primary means to effect adaptation in various forms. They usually involve computational processes incorporated within the system that trigger parametric updating and knowledge or model enhancement, giving rise to progressive improvement. This book series serves as a channel to consolidate work related to topics linked to adaptation, learning and optimization in systems and structures. Topics covered under this series include:• complex adaptive systems including evolutionary computation, memetic computing, swarm intelligence, neural networks, fuzzy systems, tabu search, simulated annealing, etc.

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Section: Fault Tolerancementioning

confidence: 99%

Self-Sufficiency of an Autonomous Reconfigurable Modular Robotic Organism

Qadir¹

2015

Adaptation, Learning, and Optimization

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“…Another widely used computer fault detection method is voting based on modular redundancy [9], [4], which is commonly used in highly reliable systems in which more than one module works redundantly to perform the same task given the same input data and the faulty module is voted out according to the module results. Analytical redundancy fault detection [13], [10] is a concept of comparing the histories of sensor outputs versus the actuator inputs to check failures. Particle filter techniques [19], [3], which have become popular recently for robot fault detection, estimate the robot and its environmental state from a sequence of noisy, partial sensor measurements.…”

Section: Related Workmentioning

confidence: 99%

Distributed sensor analysis for fault detection in tightly-coupled multi-robot team tasks

Parker

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a distributed version of our previous work, called SAFDetection, which is a sensor analysisbased fault detection approach that is used to monitor tightlycoupled multi-robot team tasks. While the centralized version of SAFDetection was shown to be successful, a shortcoming of the approach is that it does not scale well to large team sizes. The distributed SAFDetection approach addresses this problem by adapting and distributing the approach across team members. Distributed SAFDetection has the same theoretic foundation as centralized SAFDetection, which maps selected robot sensor data to a robot state by using a clustering algorithm, and builds state transition diagrams to describe the normal behavior of the robot system. However, rather than processing multiple robots' sensor data centralized on a server, distributed SAFDetection performs feature selection and clustering on individual robots to build the normal behavior model of an individual robot and the entire robot team. Fault detection is also accomplished in a distributed manner. We have implemented this distributed approach on a physical robot team and in simulation. This paper presents the results of these experiments, showing that distributed SAFDetection is an efficient approach to detect both local and interactive faults in tightly-coupled multi-robot team tasks. Compared to the centralized version, this approach provides more scalability and reliability.

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“…The most popular method for providing fault detection in robot systems is based on motion control [7], [14], [11]. Other widely used computer fault detection methods include voting based modular redundancy [16], [3], analytical redundancy [12], [8], [5] and particle filter techniques [4], [18], [2].…”

Section: Related Workmentioning

confidence: 99%