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Anomaly detection for autonomous guided vehicles using Bayesian surprise

Ozan Catal (UGent) , Sam Leroux (UGent) , Cedric De Boom (UGent) , Tim Verbelen (UGent) and Bart Dhoedt (UGent)
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Abstract
As warehouses, storage facilities and factories become more expanded and equipped with smart devices, there is a substantial need for rapid, intelligent and autonomous detection of unusual and potentially hazardous situations, also called anomalies. In particular for Autonomous Guided Vehicles (AGVs) that drive around these premises independently, unforeseen obstructions along their path-e.g. a cardboard box in the middle of a corridor or bumps in the floor-and sudden or unexpected actions executed by personnel-e.g. someone walking in a restricted area-make it hard for AGVs to navigate safely. We therefore propose a novel approach to detect such anomalies in an unsupervised manner by measuring Bayesian surprise: whenever an event is observed that does not align with the agent's prior knowledge of the world, this event is deemed surprising and could indicate an anomaly. This paper lays out the details on how to learn both the prior and posterior models of an AGV that drives around a warehouse and observes the environment through an RGBD camera. In the experiments we show that our Bayesian surprise approach outperforms a baseline that is traditionally used to detect anomalies in sequences of images.

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MLA
Catal, Ozan, et al. “Anomaly Detection for Autonomous Guided Vehicles Using Bayesian Surprise.” 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE, 2020, pp. 8148–53, doi:10.1109/IROS45743.2020.9341386.
APA
Catal, O., Leroux, S., De Boom, C., Verbelen, T., & Dhoedt, B. (2020). Anomaly detection for autonomous guided vehicles using Bayesian surprise. 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 8148–8153. https://doi.org/10.1109/IROS45743.2020.9341386
Chicago author-date
Catal, Ozan, Sam Leroux, Cedric De Boom, Tim Verbelen, and Bart Dhoedt. 2020. “Anomaly Detection for Autonomous Guided Vehicles Using Bayesian Surprise.” In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 8148–53. IEEE. https://doi.org/10.1109/IROS45743.2020.9341386.
Chicago author-date (all authors)
Catal, Ozan, Sam Leroux, Cedric De Boom, Tim Verbelen, and Bart Dhoedt. 2020. “Anomaly Detection for Autonomous Guided Vehicles Using Bayesian Surprise.” In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 8148–8153. IEEE. doi:10.1109/IROS45743.2020.9341386.
Vancouver
1.
Catal O, Leroux S, De Boom C, Verbelen T, Dhoedt B. Anomaly detection for autonomous guided vehicles using Bayesian surprise. In: 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). IEEE; 2020. p. 8148–53.
IEEE
[1]
O. Catal, S. Leroux, C. De Boom, T. Verbelen, and B. Dhoedt, “Anomaly detection for autonomous guided vehicles using Bayesian surprise,” in 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), Online (Las Vegas, USA), 2020, pp. 8148–8153.
@inproceedings{8700871,
  abstract     = {{As warehouses, storage facilities and factories become more expanded and equipped with smart devices, there is a substantial need for rapid, intelligent and autonomous detection of unusual and potentially hazardous situations, also called anomalies. In particular for Autonomous Guided Vehicles (AGVs) that drive around these premises independently, unforeseen obstructions along their path-e.g. a cardboard box in the middle of a corridor or bumps in the floor-and sudden or unexpected actions executed by personnel-e.g. someone walking in a restricted area-make it hard for AGVs to navigate safely. We therefore propose a novel approach to detect such anomalies in an unsupervised manner by measuring Bayesian surprise: whenever an event is observed that does not align with the agent's prior knowledge of the world, this event is deemed surprising and could indicate an anomaly. This paper lays out the details on how to learn both the prior and posterior models of an AGV that drives around a warehouse and observes the environment through an RGBD camera. In the experiments we show that our Bayesian surprise approach outperforms a baseline that is traditionally used to detect anomalies in sequences of images.}},
  author       = {{Catal, Ozan and Leroux, Sam and De Boom, Cedric and Verbelen, Tim and Dhoedt, Bart}},
  booktitle    = {{2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)}},
  isbn         = {{9781728162133}},
  issn         = {{2153-0858}},
  language     = {{eng}},
  location     = {{Online (Las Vegas, USA)}},
  pages        = {{8148--8153}},
  publisher    = {{IEEE}},
  title        = {{Anomaly detection for autonomous guided vehicles using Bayesian surprise}},
  url          = {{http://dx.doi.org/10.1109/IROS45743.2020.9341386}},
  year         = {{2020}},
}

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