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Abstract

The paper presents the possibilities of teaching a robot controller to perform operations of autonomous segregation of objects differing in features that can be identified using a vision system.Objects can be arranged freely on the robot scene also covered with others. In the learning phase, a robot operator presents the segregation method by moving subsequent objects held in a human hand, e.g. a red object to container A, a green object to container B, etc. The robot system, after recognizing the idea of segregation that is being done using the vision system, continues this work in an autonomous way, until all identified objects will be removed from robotic scene. There are no restrictions on the dimensions, shapes and placement of containers collecting segregated objects. The developed algorithms were verified on a test bench equipped with two modern robots KUKA LBR iiwa 14 R820.

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Authors and Affiliations

Edward Jezierski
Piotr Łuczak
Paweł Smyczyński
Dariusz Zarychta
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Abstract

Abstract. The paper introduces a neuromorphic computational approach for breathing rate monitoring of a single person observed using a Frequency-Modulated Continuous Wave radar. The architecture, aimed at implementation in analog hardware to ensure high energy efficiency and to provide system operation longevity, comprises two main functional modules. The first one is a data preprocessing unit aimed at the extraction of information relevant to the analysis objective, whereas the second one is a pre-trained recurrent neural regressor, which analyzes sequences of incoming samples and estimates the breathing rate. To ensure compatibility with neural processing and to achieve simplicity of underlying resources, several solutions were proposed for the data preprocessing module, which provides range-wise space segmentation, selection of a bin of interest (comprising the dominant motion activity), and delivery of data to regressor inputs. To implement these functions, we introduce an appropriate chirp frequency modulation scheme, apply a neuromorphic filtering procedure and use a Winner-Takes-All network for extracting information from the bin of interest. The architecture has been experimentally verified using a dataset of indoor recordings supplied with reference data from a Zephyr BioHarness device. We show that the proposed architecture is capable of making correct breathing rate estimates while being feasible for analog implementation. The mean squared regression error with respect to the Zephyr-produced reference values is approximately 3.3 breaths per minute (with a deviation of ±0:27 in the 95% confidence interval) and the estimates are produced by a recurrent, GRU-based neural regressor, with a total of only 147 parameters.
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Authors and Affiliations

Krzysztof Ślot
1
ORCID: ORCID
Piotr Łuczak
1
ORCID: ORCID
Sławomir Hausman
2
ORCID: ORCID

  1. Institute of Applied Computer Science, Lodz University of Technology
  2. Institute of Electronics, Lodz University of Technology

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