A fault detection and isolation system for cooperative manipulators

Tinós, Renato; Terra, Marco H.

doi:10.1590/s0103-17592008000400004

Cited by 3 publications

(4 citation statements)

References 37 publications

(35 reference statements)

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“…The previously discussed methods all address the level of subsystem or actuator FDIR. In [27], a Bayesian network was successfully used in order to describe the spatial relationships between the different parts of a 6-DoF robotic manipulator, which was used in order to detect failures such as blocked or deformed joints.…”

Section: [29]mentioning

confidence: 99%

“…Although there are many different techniques and studies using and evaluating advanced FDIR methods, only the remote agent experiment was actually flown and tested in space, whereas all the other projects remain theoretical simulations including a few specific on ground hardware tests. The projects [27] and [3 1] were applied to manipulators on ground. The proposed principle, however, could be transferred to the space domain.…”

Section: [29]mentioning

confidence: 99%

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Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Jaekel¹,

Scholz²

2015

2015 IEEE Aerospace Conference

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This paper presents a novel failure-tolerant architecture for future robotic spacecraft. It is based on the Time and Space Partitioning (TSP) principle as well as a combination of Artificial Intelligence (AI) and traditional concepts for system failure detection, isolation and recovery (FDIR). Contrary to classic payload that is separated from the platform, robotic devices attached onto a satellite become an integral part of the spacecraft itself. Hence, the robot needs to be integrated into the overall satellite FDIR concept in order to prevent fatal damage upon hardware or software failure. In addition, complex dexterous manipulators as required for on orbit servicing (OOS) tasks may reach unexpected failure states, where classic FDIR methods reach the edge of their capabilities with respect to successfully detecting and resolving them. Combining, and partly replacing traditional methods with flexible AI approaches aims to yield a control environment that features increased robustness, safety and reliability for space robots. The developed architecture is based on a modular on-board operational framework that features deterministic partition scheduling, an OS abstraction layer and a middleware for standardized inter-component and external communication. The supervisor (SUV) concept is utilized for exception and health management as well as deterministic system control and error management. In addition, a Kohonen self-organizing map (SOM) approach was implemented yielding a real-time robot sensor confidence analysis and failure detection. The SOM features non supervized training given a typical set of defined world states. By compiling a set of reviewable three-dimensional maps, alternative strategies in case of a failure can be found, increasing operational robustness. As demonstrator, a satellite simulator was set up featuring a client satellite that is to be captured by a servicing satellite with a 7-DoF dexterous manipulator. The avionics and robot control were integrated on an embedded, space-qualified Airbus e.Cube on-board computer. The experiments showed that the integration of SOM for robot failure detection positively complemented the capabilities of traditional FDIR methods.

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Section: [29]mentioning

confidence: 99%

Section: [29]mentioning

confidence: 99%

Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Jaekel¹,

Scholz²

2015

2015 IEEE Aerospace Conference

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“…This study deals with the most common used serial, open chained and rigid robot manipulator and only a detailed review on this type of robots is given here. Studies on other type robots can be found in (Goel et al, 2000;Tinós & Terra, 2002). Most studies on FDI for robot manipulators are based on nonlinear observer approaches.…”

Section: Literature Overview Of Model-based Fdi For Nonlinear Systemsmentioning

confidence: 99%

Model-Based FDI Schemes For Robot Manipulators Using Soft Computing Techniques

Yüksel¹,

Sezgin²

2010

Fault Detection

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“…Consequently, modelfree FDI approaches have been proposed. In [6], two artificial neural networks were employed in a fault detection and isolation system for cooperative robotic manipulators. This FDI method could be applied to cooperative systems with more robots.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and identification based on combining logic and model in a wall-climbing robot

Jiang

Wang

Fang

et al. 2009

J. Control Theory Appl.

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A combined logic-and model-based approach to fault detection and identification (FDI) in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events (elementary causes), which can lead to a root event (a particular fault). Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.

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A fault detection and isolation system for cooperative manipulators

Cited by 3 publications

References 37 publications

Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Utilizing Artificial Intelligence to achieve a robust architecture for future robotic spacecraft

Model-Based FDI Schemes For Robot Manipulators Using Soft Computing Techniques

Fault detection and identification based on combining logic and model in a wall-climbing robot

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