Abstract
Research in man-made systems capable of self-diagnosis and self-repair is becoming increasingly relevant in a range of scenarios in which in situ repair/diagnosis by a human operator is infeasible within an appropriate time frame. In this paper, we present an approach to the multi-robot team diagnosis problem that utilizes gradient-based training of multivariate Gaussian distributions. We then evaluate this approach using a testbed involving modular mobile robots, each assembled from four electromechanically separable modules. The diagnosis algorithm is trained on data obtained from two sources: (1) a computer model of the system dynamics and (2) experimental runs of the physical prototypes. Tests were then performed in which a fault was introduced in one robot in the testbed and the diagnostic algorithm was queried. The results show that the state predicted by the diagnostic algorithm performed well in identifying the fault state in the case when the model was trained using the experimental data. Limited convergence was also demonstrated using training data from an imperfect dynamic model and low data sampling frequencies.
Original language | English (US) |
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Pages (from-to) | 1069-1090 |
Number of pages | 22 |
Journal | International Journal of Robotics Research |
Volume | 27 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2008 |
Keywords
- Diagnosis
- Fault diagnosis
- Gradient-based training
- Mobile robot
- Modular robot
- Multi-robot system
- Particle filters
- Robot team repair
- Robotics
- Self diagnosis
- Team diagnosis
ASJC Scopus subject areas
- Software
- Modeling and Simulation
- Mechanical Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
- Applied Mathematics