Robotic Cleaning System for Salmon Slaughterhouses

The purpose of this Master's thesis is to look at possible designs for a robotic cleaning solution for salmon slaughterhouses due to the daily need for cleaning and thus high labor costs, and simulate these solutions. In addition, the use of computer vision to aid in such a task is investigated. This thesis is part of a research project that aims at being able to clean a whole slaughterhouse. The focus on this thesis was mainly on cleaning an electric stunner, but many of the challenges with cleaning such a component are transferable to cleaning a whole slaughterhouse. The main emphasis of this Master's thesis has been on developing different designs for the robotic cleaning solution. Finding a way to move a robot around in a salmon slaughterhouse is a big challenge, and various solutions to this have been developed and investigated. This thesis has both looked at alternatives for the robot, and also looked at smart solutions for the suspension of a robot. Both existing components already on the market have been evaluated, and building certain components from scratch have been investigated. A custom, modular robot have also been designed, with the goal of making a robot that is lighter and has a longer reach than any other robot in its weight class. Some of the designed solutions have been simulated, and it can be concluded that while probably none of the designed solutions will be the final solution when the project is done, some of the designs will greatly influence the next steps for the project. Further work will require trail and error and prototyping to reach a final design. Another aspect of the robotic cleaning is the robot trajectories. Cleaning is usually performed every day after production has stopped. In order to be able to clean even though a component has been moved, requiring new robot trajectories, computer vision has been tested to see if the position and orientation of components can be established with such an accuracy that the robot trajectories can be updated according to the computer vision data, and resume the cleaning. It can be concluded that computer vision can be accurate enough to calibrate new trajectories if a component is moved, given a good enough sensor. It was also tested to see if computer vision can be used to find unwanted obstacles in the environment. The experiments showed good results, and it can be concluded that unwanted objects can be found as long as they are not too small. To alleviate some of the problems with manually programming the hundreds of robot commands necessary to clean a whole electric stunner, some time was spent investigating the possibility to generate robot trajectories automatically from a CAD model of the electric stunner. This showed promising results for a simple geometry like a box on a table, but the complex geometries for the components in a salmon slaughterhouse proved to be difficult. Further work would be required to achieve a satisfactory result

Vision system for quality assessment of robotic cleaning of fish processing plants using CNN

A vision system has been developed for automatic quality assessment of robotic cleaning of fish processing lines. The quality assessment is done by detecting residual fish blood on cleaned surfaces. The system is based on classification using convolutional neural networks (CNNs). The performance of different convolutional neural network architectures and parameters is evaluated. The datasets that simulate various conditions in fish processing plants are generated using data augmentation techniques. Tests using further augmented training data to increase the performance of the neural network are performed, which results in a substantial increase in performance both compared to the color thresholding technique and the same neural network architecture without augmented training data. The performance of the system is validated in experiments in an industrial setting. Pub

An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems' critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions

Experimental study of effectiveness of robotic cleaning for fish-processing plants

This paper presents the development and experimental testing of the effectiveness of a robotic cleaning system for fish processing plants. The processing of fish introduces a substantial risk of bacterial contamination, which can cause the spoilage of fish and pose a threat to consumers’ health. Good operational hygiene and precautions, in addition to regular cleaning of the processing plants, are necessary for the reduction of the risk of contamination. The state-of-the art cleaning techniques currently include manual cleaning operations of fish processing plants. The experiments of robotic cleaning presented in this paper were performed in two rounds. First, a test using a conventional low-cost industrial robot mounted on a vertical linear axis was used. As the results from this test seemed promising, a second robotic system was built aiming at a more industrialized version. This system consisted of a serial manipulator, tailored for the task, mounted on a horizontal transportation system, and a comparison was conducted between the cleaning performed by human operators and that performed by the robotic system. An electrical stunner with a connected conveyor belt, which is a typical installation for salmon processing plants, was experimentally inoculated with a cocktail of fish-spoilage bacteria that were allowed to develop a biofilm. Back-to-back cleaning trials with biofilms of Pseudomonas fluorescens, Pseudomonas putida, and Photobacterium phosphoreum confirmed that the industrialized robotic prototype performed equally well or better than the conventional manual cleaning procedure currently used in the industry. The results demonstrate that a robotic system can deliver satisfactory results in the cleaning of fish processing plants, thereby minimizing the potential for the spread of contamination. The proposed robotic concept allows for an automated cleaning system, reduced human labor, increased profitability for the industry, and better stability of the cleaning process