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Paper TuA06.5

Censi, Andrea (California Institute of Technology), Hakansson, Magnus (Lund University), Murray, Richard (California Institute of Technology)

Fault Detection and Isolation from Uninterpreted Data in Robotic Sensorimotor Cascades

Scheduled for presentation during the Regular Session "Applied Machine Learning" (TuA06), Tuesday, May 15, 2012, 09:30−09:45, Meeting Room 6 (Oya'te)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Learning and Adaptive Systems, Sensor Fusion, Wheeled Robots

Abstract

One of the challenges in designing the next generation of robots operating in non-engineered environments is that there seems to be an infinite amount of causes that make the sensor data unreliable or actuators ineffective. In this paper, we discuss what faults are possible to detect using zero modeling effort: we start from uninterpreted streams of observations and commands, and without a prior knowledge of a model of the world. We show that in sensorimotor cascades it is possible to define static faults independently of a nominal model. We define an information-theoretic usefulness of a sensor reading and we show that it captures several kind of sensorimotor faults frequently encountered in practice. We particularize these ideas to the case of BDS/BGDS models, proposed in previous work as suitable candidates for describing generic sensorimotor cascades. We show several examples with camera and range-finder data, and we discuss a possible way to integrate these techniques in an existing robot software architecture.

 

 

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