Anomaly Detection based Post Gripping Perception for Industrial Logistics Robotics
type: CreativeWork
description: The adoption of low volume high mix manufacturing lines in global production sites has lead to a great need for flexible automation solutions. Flexible intelligent robots as an example are increasingly deployed in new industrial environments, such as the intra-logistics, in order to increase the automation level of the mentioned manufacturing lines. The lack of structure and the high dynamic of such industrial environments lead to potential performance deterioration related to unexpected events that are not recognized by the robot. Detecting such changes in the robot environment is believed in this thesis to be possible with anomaly detection techniques which allows the robot to react to them either autonomously or with user interaction. In other words, this work examines the impact of integrating anomaly detection in productive intelligent robots on the stability and quality of the automated process. A modular framework, called the post gripping perception framework, has been developed in this research to define a methodical integration of image anomaly detection in intelligent manipulation robots, whose software is based on image object detection for pick and place tasks. Adding anomaly detection modules for the planning, monitoring and verifying of the post gripping steps has been identified to be efficient in enhancing the stability and quality of robotic processes. The framework also defines the nature of anomaly detection approaches needed in the different modules, namely structural anomaly detection in the planning and verifying modules for quality enhancement and logical anomaly detection in the monitoring and verifying modules for stability increase. The challenge of collecting rare anomaly data in productive robotic processes has been considered in this thesis by introducing an approach to transform state-of-the-art semi-supervised anomaly detection into unsupervised anomaly detection by eliminating the anomaly score thresholding step that is highly depend…
abstract: The adoption of low volume high mix manufacturing lines in global production sites has lead to a great need for flexible automation solutions. Flexible intelligent robots as an example are increasingly deployed in new industrial environments, such as the intra-logistics, in order to increase the automation level of the mentioned manufacturing lines. The lack of structure and the high dynamic of such industrial environments lead to potential performance deterioration related to unexpected events that are not recognized by the robot. Detecting such changes in the robot environment is believed in this thesis to be possible with anomaly detection techniques which allows the robot to react to them either autonomously or with user interaction. In other words, this work examines the impact of integrating anomaly detection in productive intelligent robots on the stability and quality of the automated process. A modular framework, called the post gripping perception framework, has been developed in this research to define a methodical integration of image anomaly detection in intelligent manipulation robots, whose software is based on image object detection for pick and place tasks. Adding anomaly detection modules for the planning, monitoring and verifying of the post gripping steps has been identified to be efficient in enhancing the stability and quality of robotic processes. The framework also defines the nature of anomaly detection approaches needed in the different modules, namely structural anomaly detection in the planning and verifying modules for quality enhancement and logical anomaly detection in the monitoring and verifying modules for stability increase. The challenge of collecting rare anomaly data in productive robotic processes has been considered in this thesis by introducing an approach to transform state-of-the-art semi-supervised anomaly detection into unsupervised anomaly detection by eliminating the anomaly score thresholding step that is highly depend…
author: Zoghlami, Firas
language: eng
date published: 2025-01-01T08:00:00Z
date modified: 2026-07-24T03:47:57Z
provider: University of Plymouth
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Plymouth