PRE2022 3 Group9

Eryk Oyku Siiri Matilda Nika Maud

Topic: We want to do something more prototype based

Everyone must come up with sources of research for the Deep Sea Passive collection robot

Problem statement and objectives: We want to design an autonomous robot for deep sea garbage collection.

Maud: I found the following sources, which, based on their abstract, are about under water waste collection or surface waste collection. When I have read them I will add a summary.

• An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning. DOI: 10.1109/TITS.2022.3190278
  • This article discusses a method for surface garbage cleaning robots to find a good route to collect all the garbage in an environment that contains obstacles. It is assumed that it is known where all the garbage and obstacles are located.
  • For the path planning, first the order is determined in which the robot will visit all the pieces of garbage and then the route is determined.
    • To determine the order in which the robot will visit the garbage, the problem is modeled as a Traveling Salesman Problem. However, instead of the Euclidian distance, that does not take the obstacles into account, the authors used a heuristically guided FM* based distance (FM* stands for fast marching, I don't know exactly what FM* distance is, but it makes sure that the path goes around the obstacles, creates a smooth path and is fast to compute. FM* is similar to Dijkstra, so it computes the shortest distance form one point to the other, but it does not uses a partial differential equation to estimate the distance).
    • For the route of the robot, it is not always possible to chose the shortest path, because the robot needs to keep enough distance to the obstacles to not accidentally bump into them if they move and the robot might not be able to follow all paths, for example because it is not able to make a curve smaller than a certain diameter (the angle is to small). To make sure that the robot does not need to make to steep curves, a Gaussian filter was applied. The bigger the sigma in this filter, the bigger the curves and the further the robot stayed away from the obstacles. (There are formula's in the article about exactly how they applied the Gaussian filter, but I could not follow exactly what they were doing).
  • During the execution of the plan, the robot can get new information about the location and movement vector of the garbage it is currently heading for. If the garbage has shifted due to the current, the robot uses a trained neural network to determine its new route. For as far as I understand, the location of the obstacles and possible movement of the obstacles is not taken into account. The system does not re-compute the order in which it collects the garbage, even if the new situation as a different optimal collection order.
  • There are a lot of formulas describing what they do, but they often do not make it clearer. If we need them we can look at them later.
• A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot. https://doi.org/10.1631/FITEE.2100473
  • This article discusses an algorithm for detecting and classifying garbage underwater and determining its location compared to the robot.
  • This detection needs to happen fast and in real time, because the robots environment will be constantly changing.
  • The researchers use the YOLOv4 network, which is a one stage neural network that takes in the image and returns both the class and location of the objects in the image. The researchers pruned to reduce the number of calculations needed.
  • The network was trained on a data set, where it had to distinguish nets, plastic bags and stones. The data consisted of images from multiple sides and in multiple conditions made in a swimming pool. For the robot to perform in the real world, it needs to be able to detect more different kinds of garbage and also things like fish and under water plants. More realistic images could also pose a problem for this data set.
  • Pruning is used to significantly increase the detection speed. Pruning is achieved by not calculating the channels with the smallest contributions. This method works now, because there are very few categories, however, for a more realistic model, this would not work anymore.
• Open-Frame Underwater Robot Based on Vector Propeller Control. DOI: 10.1109/NetCIT54147.2021.00044
• DAMONA: A Multi-robot System for Collection of Waste in Ocean and Sea. https://doi.org/10.1007/978-981-16-8721-1_15/
• Design of water surface collection robot based on deep sea cage culture. DOI: 10.1088/1742-6596/2229/1/012005

Research technology:

• https://www.itopf.org/knowledge-resources/documents-guides/tip-05-use-of-skimmers-in-oil-pollution-response/
  • There are different kinds of skimmers. Some are only useful for oleophilic oils, oils that stick to certain surfaces, where the oil sticks to a part of the skimmer and is scraped off and collected. Other skimmers use different methods to suck up the oil, similar to a vacuum cleaner or using gravity. Oleophilic skimmers are sometimes quite resistant to debris, dependent on the model, but non oleophilic skimmers all can be clogged by medium and large debris, and only some are not clogged by small debris.
  • Problems encountered by skimmers:
    • Rough water
    • The oil is spread over a large surface, low encounter rate. Booms can help concentrate the oil
    • Debris
      • Debris screens get blocked by oil or debris
    • The skimmers are not selective enough, causing the storage to be filled up quick with a lot of water as well
    • The oil has a to high viscosity

Nika:

• Review of Underwater Ship Hull Cleaning Technologies | SpringerLink (tue.nl)
  • This paper presents a comprehensive review and analysis of ship hull cleaning technologies. Various cleaning methods and devices applied to dry-dock cleaning and underwater cleaning are introduced in detail,
  • Using the analysis of these technologies, we could take the positives and negatives into account in our research.
• Analysis of a novel autonomous underwater robot for biofouling prevention and inspection in fish farms | IEEE Conference Publication | IEEE Xplore (tue.nl)
  • Biofouling is a challenge for finfish farming as it can impact cage stability and fish health. Amongst others, current strategies against biofouling rely heavily on removal of biofouling using in-situ pressure cleaning of nets. The cleaning waste is released into the water where it can impact the health of the cultured fish
  • We can take the health of fish into account when designing the robot

Siiri:

• Plastic Waste is Exponentially Filling our Oceans, but where are the Robots? | IEEE Conference Publication | IEEE Xplore

• Robotic Detection of Marine Litter Using Deep Visual Detection Models | IEEE Conference Publication | IEEE Xplore

• Deep learning-based waste detection in natural and urban environments - ScienceDirect

Week 2:

New problem statement

Oil skimmers, used to collect oil from the surface of the water during oil spills, get easily clogged by floating plastic and debris. By equipping the skimmers with a camera, and by using neural networks to classify images, the skimmers could become fully automated, moving away from debris without the need for human intervention.

Users: Government organizations that focus on the environment.

What do they require:

Approach, milestones and deliverables: start with literature research to see what already exists, identify points that research is still needed and propose a way to combine multiple (existing) technologies to create a working robot.

Who's doing what?:

Matilda: image recognition model with TensorFlow

- Sort training data by size/looks of plastic

- Goal: 100 pictures

- https://www.tensorflow.org/tutorials/images/classification

• DeepPlastic: project in which deep learning and computer vision has been used to identify plastic underwater. Researchers found that, compared to YOLOv4 and YOLOv5, the deep learning model was sufficiently accurate and fast enough to be used in applications like underwater autonomous plastic collectors.
• Another study applied deep learning to estimate the volume of macro-plastics in oceans, proposing a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.
• Vito remote sensing: company using satellites and AI vision for multiple applications, including detection of marine plastic litter.

Nika:

• Research to problem statement
  • Oil skimmers, used to collect oil from the surface of the water during oil spills, get easily clogged by floating plastic and debris. By equipping the skimmers with a camera, and by using neural networks to classify images, the skimmers could become fully automated, moving away from debris without the need for human intervention.

• Definition of oil skimmers: devices that separate oil from water in order for it to be collected for the purposes of recovery of remediation. Skimmers can be installed into two different ways, either floating or fixed/ mounted.

• Different types of oil skimmers.
  • Oleophilic: using an element to which the oil adheres. The oil is wiped from the oleophilic surface and collected in a tank
    • Drum skimmers: wiper blades remove the oil from the rotating drums, depositing it into the collection trough where it is pumped to a storage location
    • Brush skimmers: stiffness and density of the bristles impacts the amount and type of oil they can recover.
    • Disc skimmers: Capable of recovering high volumes of oil with very little water
    • Belt skimmers: a belt that attracts the oil which is then scraped clean and collected in a tank. Remove all kinds of floating oil
  • Non-Oleophilic:
    • Weir skimmers: oil flows into the central hopper where it's pumped to storage. Used for rivers, lakes, etc.

• Limitations to oil skimmers
  • Trash may block the skimmer to become stuck.
  • Effectiveness of different oils
  • Skimming direction

• Existing robots
  • 1. Unmanned floating wase collecting robot
    • Collecting floating trash in oceans using a robot hand and collecting it using a belt to the bin.
    • Assist humans in removing trash, not autonomous.

• Questions for next time:
• We have to decide which type of oil skimmer we focus on.
  • Research how the oil skimmer can become stuck
  • Provide research to distinguish our prototype with existing robots

References