PRE2022 3 Group9

Implementation and Simulation of Debris Recognition for Autonomous Drum Oil Skimmers

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Group members

Name	Student Number	Email
Eryk Gruszecki	1731483	e.s.gruszecki@student.tue.nl
Mathilda Fogato	1656376	m.fogato@student.tue.nl
Oyku Sanlibayrak	1654519	o.a.sanlibayrak@student.tue.nl
Maud van Bokhoven	1664387	m.m.v.bokhoven@student.tue.nl
Siiri Jokiranta	1614207	s.h.jokiranta@student.tue.nl
Nika Tersteeg	1750828	n.n.q.y.tersteeg@student.tue.nl

Introduction

(Oyku) Ocean and other major body of water oil spills have a terrible impact on the ecosystem and people's health. With consequences ranging from physical pain to changes in behavior and reproduction, the hazardous compounds present in crude oil can disrupt marine ecosystems both temporarily and permanently. Fish, marine mammals, and birds who come into touch with the oil spill or consume polluted prey may experience particularly severe effects. Oil spills can harm human health in addition to having a harmful effect on wildlife. For individuals working to clean up oil spills, exposure to the hazardous chemicals found in oil can lead to respiratory troubles, skin irritation, and other health problems.For individuals working to clean up oil spills, exposure to the hazardous chemicals found in oil can lead to respiratory troubles, skin irritation, and other health problems. Economic losses may also occur in coastal areas as a result of the fishing and tourism industries. As a result, it is crucial that action be done to stop and lessen the harm caused by oil spills. The most concerning oil spills are primarily maritime ones. Since oil is a major source of energy, it is utilized frequently and all over the world, which makes spills like these more frequent. The majority of oil that is spilt remains on the surface and spreads out quite rapidly. Although conventional methods of oil spill cleanup, such the use of booms and skimmers, have been successful in removing oil from the ocean, they are frequently expensive, time-consuming, and heavily labor-intensive. One of the biggest problems oil skimmers face is clogging by floating debris, which reduces their efficiency and efficacy in oil spill cleaning efforts. As a result, the goal of this study paper is to delve more into a potential remedy for the problem of debris clogging oil skimmers, which involves fitting the skimmers with cameras and utilizing neural networks to sort trash photos in the ocean.

Approach / methodology

(Nika)

The research will focus on the stimulation of the oil spills in oceans and the performance of oil skimmers in cleaning the oil spills. An attempt to simulate an implementation of the suitable camera system to oil skimmers which can lead to a more effective way of cleaning oil spills in the ocean. This research will be conducted mainly by literature research, simulating the oil skimmer and contacting oil skimming companies and finally creating a simulation.

First of all, literature review will be needed to obtain relevant information on oil spills, the impact on the environment, current methods and existing oil skimmers. This information is needed to provide a clear overview and possibly new insights to design the autonomous oil skimmer.

Second, by contacting oil skimming companies another view will be obtained about the effectiveness of their current methods and machines. This will help to understand the perspective of the people in the industry, which will give a deeper understanding on the problems that oil skimmers face.

Third, we have chosen to use an image recognition system for plastics in oceans. By creating a database of images the robot will learn and optimize to minimize false positives and false negatives by learning by recognition plastics in the ocean and the distinction between plastics covered in oil, big chunks of plastics etc.

Fourth, the performance metrics play a big role in designing the robot to evaluate the performance and the effectiveness of the autonomous oil skimmer and compare the measurements with a "regular" oil skimmer. Performance metrics such as speed of cleaning, price/ production costs, lifespan, sustainable battery and the percentage of oil recovery (not sure about this).

Fifth and last, taking all these four aspects together in creating a simulation of the autonomous oil skimmer to obtain an insight how to robot eventually will work in a realistic environment. The simulation will be conducted using Unity in order to vary different conditions and aspects to test and optimize the robot.

Limitations:

- it is hard to design a large database for machine learning

- it is difficult to take every floating object into account. Think about the distinction between a dead fish and a floating piece of wood.

- what are we going to do when we do find a piece of plastic? avoid the plastic or pick it up?

- how are for example vessels going to recognize the robot in the sea or see the robot on a map? Is everyone be able to track the robot?

- How are we going to prove that this is more effective and safe than a regular skimmer, since it is autonomous?

- only using a simulation to prove the effectiveness and quality is weak. further research is needed for confirmation

Reliability and validity:

- internal validity: no influences by other factors

- external validity: generalizable/ results can be applied to other situations

- internal reliability: consistency of results across items within a test

- external reliability; the extent to which a measure varies from one user to another

Analysis technique:

Analyzing in Unity. How are we going to analyze the simulation? What is important to look at? What is our goal?

Reason for investigation -> problem statement

Problem Statement

(Nika and Oyku)

Oceanic oil spills can harm marine life and their ecosystems, among other serious environmental effects. The research conducted by R.Manivel and R. Sivakumar in the publication "Boat type oil recovery skimmer" can be a useful source to comprehend in order to respond to the question of why focus on the oil spills. According to the publication, oil leakage is becoming a bigger issue due to an increase in ship mishaps and oil transportation, which endangers people's health. This is why it's so important to act and protect the environment as soon as possible.

Modern methods for cleaning up oil spills, such as ordinary drum oil skimmers, are efficient in removing oil from the water's surface. Oil and water are separated by a spinning, floating drum in the water. When the oil sticks to the drums, wiper blades have to remove the oil from the drums. Since they are portable, dependable, and effective, drum oil skimmers are said to be an effective oil skimmer. The oil skimmer, however, has its limitations.One of the main issues that stood out when examining the difficulties that oil skimmers encounter when collecting oil is the buildup of debris inside the machine, which wastes time and money. According to the School of Maritime Studies and Transport's research, this issue with plastic debris entering the machine and clogging the drum oil skimmers occurs quite frequently. In that situation, maintaining the oil skimmer will take a lot of time. Since oil disperses swiftly in water, it is critical that the oil be cleaned up as soon as a spill occurs.Also, as time goes on, cleaning up the spilled will become more and more difficult. To reduce the impact of the oil leak, it is crucial to respond and clean as soon as you can.

Minimizing maintenance and oil extraction speed are necessary in order to achieve this. Using a machine vision camera with image recognition capabilities that can identify debris and disregard it is one potential solution to this issue. This leads to extended working hours for the skimmer without the assistance of a second worker. A novel technology that has the ability to avoid trash while efficiently cleaning up oil spills is an automated drum oil skimmer outfitted with plastic identification software. Unfortunately, it is unknown how well this technique performs in practical situations when compared to a typical drum oil skimmer. This project's goal is to compare an autonomous drum oil skimmer with plastic detection software against a standard drum oil skimmer in order to determine which one performs better in terms of oil recovery, efficiency, and environmental impact.

Final Goal

(Eryk)

The goal of this robotics simulation is to demonstrate the effectiveness of integrating object recognition and navigational path planning in oil skimmers for the purpose of improving the amount of oil collected and decreasing maintenance times. By creating a proof of concept, we aim to showcase the benefits of this technology and provide a basis for future development and implementation in the oil spill response industry. Through simulation, we will test the system's ability to accurately recognize oil and navigate a path that maximizes oil collection while minimizing equipment wear and tear. Ultimately, the goal is to provide a more efficient and effective method for oil spill cleanup, reducing the impact on the environment and minimizing the costs associated with cleanup efforts.

Weekly Tasks

Functionalities

What is an oil spill?

Oil spills can be defined as the leak or spillage of petroleum, which is a mixture of hydrocarbons in a gaseous, liquid or solid form, into the environment. They are common in the sea and have been occurring for a long time, causing life-threatening scenarios. Some oil spills occur naturally due to the activity of earth movements that cause liquid or gas to escape on the surface. However, the most oil spills occurs due to human involvement such as accidents in which tankers release oil, but even intentional spills were made in history to.

Nowadays, shipping and environmental regulations are made to prevent oil spills, but still many oil spills happens. According to ITOPF, which is an company that maintains a database of oil spills from tank vessels, the average number of oil spills from 1970's till 2022 is reduces by almost 90%

Figure 1 shows that the number of medium and large tanker spills is reduced from an average of 78.8 to 5.7 oil spills per year. Furthermore, ITOPF stated that the main causes of oil spills were caused by collisions and groundings.

Oil distributes easily across the water since it is The size and location of an oil spill are important factors in the overall impact. Larger spills require more oil to clean up and the process is expensive. However, it happened that small oil spills incurred greater overall costs than larger spills, due to the lack of equipment and the difficulty of detecting such a small leak. Moreover, the cost of the spill depends on the distance from the coast, as the impact of the spill is greater when the oil hits the coast . Moreover, the type of oil and the concentration of chemicals play a role in the severity of the leak. In short, the impact of an oil spill depends on a lot of factors. The main factors are the size, location and the type of oil, but also the cause the of accidents, the type of oil skimmer, the currents / winds and the effectiveness of the clean-up are important for the total costs.  

Another important aspect is the planning and the decisions that are needed to be made concerning making predictions in the distribution of oil when an oil spill occurs. Computer and mathematical models could provide insights to minimize the risks and impacts and in effectively clean up the oil with for instance a searching algorithm. The size and the location of an oil spill are important factors for the impact. In addition, the type of oil and the concentration of the amount of chemicals plays a role in the seriousness of the leak.

What are oil skimmers?

(Siiri) Oil skimmers are devices that are used to clean up oil spills by collecting oil and separating it from water. The advantage of oil skimmers compared with other methods of cleaning up oil spills is that the oil that is collected can be reused. Other methods of cleaning up oil spills include oil booms, sorbents and burning oil. As the focus of the course is on robotics oil skimmers were chosen as the method of cleaning up oil.

The type of skimmer that is the most effective at collecting oil depends on the type of water body (sea, lake), the type of oil (heavy, light) and environmental conditions such as wind speed, the strength of surface waves and the presence of debris. There are three main types of oil skimmers: weir skimmers, oleophilic skimmers and suction skimmers. Weir skimmers float on the surface of water and use a dam at the oil-water interface to separate oil from water whereas suction skimmers use a vacuum to pump the oil into a storage tank. Weir skimmers and suction skimmers are only effective in calm waters and get easily clogged by debris (Dhaka, 2021).

Oleophilic skimmers are the most effective type of skimmer for collecting oil in the ocean. This type of skimmer uses an oleophilic material that absorbs oil to separate oil from water. Drum skimmers are a specific type of oleophilic skimmer where a rotating drum is coated with an oil-absorbing material. Some oleophilic skimmers can deal with small amounts of debris but drum skimmers are sensitive to being clogged (ITOPF, 2021).

Drum skimmers were chosen as the type of oil skimmer to focus on because they are commonly used for cleaning up oil spills in an ocean environment and they face the problem of getting clogged by debris.

Debris

(Siiri) Most oil skimmers are not effective at collecting oil when there is a lot of debris present because debris can clog the intake of the skimmer and prevent it from pumping oil. This can result in the skimmer breaking down or someone having to manually remove the debris from the skimmer which increases the amount of time it takes to clean up an oil spill (Dhaka, 2021). In addition to this, more people are needed to operate the skimmer as it has to be constantly observed to prevent it from getting clogged. In areas with a high amount of debris such as harbors, the debris must first be removed before oil skimmers can be used to collect oil which can further increase costs and the amount of time it takes to clean up the oil spill. (Nadeau, 1977) The main ways to deal with debris are to either manually remove the debris or to use an oil skimmer that collects the debris together with the oil.

Specifications of Oil Skimmers

Types of oil skimmers

(Oyku) There are several different varieties of oil skimmers, each with unique advantages and limitations. Beginning with belt skimmers, these machines use a rotating belt to collect oil from the water's surface and transport it to a collection container. They perform best when removing sizable amounts of oil from bodies of open water, such as lakes or harbors. Drum skimmers revolve a drum in order to gather oil from the water's surface. They are good in removing both light and heavy oils, and they operate well in both calm and choppy waters. The best place to use disc skimmers is in shallow water since they use a rotating disc to remove oil from a surface. Weir skimmers are best employed in calm environments like ponds since they collect oil off the water's surface.The drum skimmers will be the subject of this study, though. The cause behind that can be expressed in two key points: The ability of the drum oil skimmers to function in open ocean and sea is one of them. Focusing on a drum-type oil skimmer is an excellent idea because this research is concerned with debris in the open sea, which is a much more complex environment for the oil skimmer. Moreover, the drum oil skimmer is mobile. It can travel independently to get oil. This is an important factor to consider when selecting the oil skimmer that will be the subject of this study because our exploration of the oil skimmer needs to be mobile in order to detect debris in the ocean using the image recognition technology that will be used in the camera system and to ignore the debris that might clog the skimmer.

How long does it take to clean oil with a drum skimmer?

(Nika) As it is shown that the amount of medium, 7-700 tons, and large spills, >700 ton has been decreased, but over 80% of spills since 1970 are small oil spills, <700 ton . Since small oil spills occurs often, it is important to look further into these and questioning the impact of such a small oil spill compared to medium or large spills.

(1) The article "Calculation of oil droplet size distribution in ocean spills" by I. Nissanka and P. Yapa reviews the impact of an single droplet of oil in the ocean based on model predictions

As it is said before, using models and simulations could make predictions in the possible risks and according to the I. Nissanka and Y. Yapa, the calculation of an oil Droplet Size Distriubtion (DSD) are essential in model predictions of ocean oil spills. The calculation of a droplet size can be split up into equilibrium models and droplet population dynamic models with both promising promising, but the equilibrium model is preferred since it can be used for field situations. As believed by I.Nissanka and Y.Yapa, modeling this DSD is complex and contains several processes but it is impossible to include all these processes in the model.

Some important equations:

Description	Equation
The maximum stable droplet size surface to breaking waves	VMD /d0 = δ(1 + 10 Oh) ^p We^q
The collision frequency of droplet size class i with an eddy (current) λj	θiλj = NiNλjSi,λj sqrt(u'i^2 + u'λj^2)
The frequency of droplet collision due to turbulent fluctuations	θij = NiNjSij sqrt(u'i^2 +u'j^2)
The coalescence efficiency	Pij = exp(-Tij/ Tau/ij)
Shear rate S by a droplet	Rmax =
Volume of oil into the water when waves break into oil area	V(t) = FsAs(t)h(t)

(2) J. Montewka attempts to design a probabilistic and systematic model to estimate the costs of clean-up operations for the Gulf of Finland in "A probabilistic model estimating oil spill clean-up costs - A case study for the gulf of Finland" .

The author presents a model based on Bayesian Belief Networks that analyses the cleanup costs for the Gulf of Finland in risk framework. A cost model includes utility-, decision-, independent- and conditional variables. In addition, the oil type, spill size, season, wave height, evaporation, effect of booms, time for vessel to arrive, time to collect the oil, oil-combating efficiency, number of vessels, manual and machine costs.

In the first scenario, an oil spill of 5000 tons of medium oil the total clean-up costs: 12.1 M

Second scenario: oil spill of 30000 tons of heave crude oil: 95 M.

Doesn't include socioeconomic, environmental and waste management costs.

The model allows the user to select the location of an oil spill, its size, type of oil, time to collect the oil and the model calculates the total costs of clean-up operations.

In our research we can use this source to prove that the total costs to clean up an oil spill will decrease when the recovery time will decrease since the time to collect the oil is a factor in the model.

(3) "Performance of rotating drum skimmer in oil spill" recovery by A. Hammoud and M. Khali investigates the effectiveness of the oil skimmer and finally predict the oil recovery rate of the device.

"According to the authors, the rate of the maximum oil recovery is dependent on drum rpm and oil viscosity. Oil increases with oil thickness for both low and high viscosity oils. Furthermore, oil recovery rate decreases with wave height for both low and high viscosity oil and increases with current velocity."

An experiment was conducted in which a rotating drum skimmer was tested in a water reservoir spilled with oil. For each test, the oil thickness, which varied between 10 to 40 mm, and the distance between the drum and the surface were measured.

In another similar experiment, the oil recovery rate and the oil recovery efficiency were measured.

The results show the effects between the rotation speed and the recovery rate in crude oil. The main factors to indicate the performance of the drum skimmer are the speed of the rotating drums and the drum diameter. This can be explained as the speed of the rotation increases, the oil recovery rate increases since the drum contact area with the oil increases. In addition, the oil recovery rate also increases when the distance between the oil and the surface decreases.

The same results were seen for diesel oil and SAE 10 w oil

Empirical equation:

f1(Q; d; L; o; h; t; r; m; s) = 0 where Q = the oil recovery rate (m3 /s), d = drum outer diameter (m), L= drum length (m), o = rotational speed (rad/s), h = height of the drum centre above the oil/water interface surface (m), t = oil film thickness (m), r =oil density (kg/m3 ), m = dynamic viscosity of oil (Pas), and s = oil surface tension (N/m).

The oil recovery rate Q= 0:026521 x (w ^1,23 x d1,75 x h0,17 x t 0,1 x L x m0,65) / (r0,21 x s0,44 )m3 /s.

Operational parameters for recovery rate

Speed:

In the article “Effect of operational parameters on the recovery rate of an oleophilic drum skimmer” written by V.Broje and A. Keller shows the effects of the different parameters on the recovery rate. Looking at the speed of the oil drum skimmer, the drum rotational speed was tested for 30, 40 and 65 rpm (rotations per minute). Varying the speed controlled the encounter rate of the oleophilic surface with the oil front. 30 rpm represents the minimal rotation speed and 65 rpm the maximal speed that could be achieved for average drum skimmers. A higher speed emulsifies the oil to a greater extent. It shows that the amount of entrained water will increase, especially in the case of thin oil slicks and/ or viscous oils. The amount of recovered oil can be increased by 50-100% using higher rotational speeds. 40 rpm seemed to be near the optimal rotational speed above which the drum starts to entrain significant amounts of water which results in less emulsification. If storage capacity is not limited, drums should be operated at their maximum speed.

In another experiment by Walaa Sabbar et al (2021) investigated in their experiment in which the temperature, viscosity, oil slick thickness and drum rotational. In addition, these parameters were tested for different type of oils such as light diesel, crude oil and heavy diesel oil. The results of the optimal speed for the rotations in shows that the recovery rate is proportional to the increase in the drum rotation speed and predicted in their experiment that 56 rpm  was the optimum rotation speed. Light diesel shows that this type of oil had the best recovery rate which increases with decreasing viscosity.

USE Assessment

When designing a new type of drum skimmer it is necessary to look at the USE assessment and take these different aspects into account.

User

Different users are involved such as oil spills companies, government agencies, oil and gas companies, environmental organization and research institutions.

Environmental companies

Oil spills have negative impacts on public health, drinking water, natural resources, ecology and the economy. As the oil industry is huge and unavoidable, large quantities of oil are used and transported, sometimes resulting in an oil spill, putting public health and the environment at risk. The U.S. Environmental Protection Agency's Oil spill Program plays an important role in the protection of oil spills that occur in and around the U.S. The organisation tries to prevent these oil spills and published a booklet containing information about oil spill which include potential effects. EPA proposed different techniques that may be used after an oil spill to minimize the impacts. The main response technique is the "mechanical containment or recovery". This includes the recovery equipment such as barriers, skimmers, booms and sorbent materials.

When there is a new design of an oil skimmer which shows to be more effective and efficiency, environmental organizations such as EPA could implement this as a standard practice for oil spill response.  This could lead to a better environment as EPA aim to minimize the negative impacts of oil spills.

Society

Environment

According to A. Clifton (2014), Petroleum is one of the most consumed raw material as an energy source and therefore transported in significantly large oil tankers. The annual estimated amount of petroleum that is spilled is between 10 million and 20 million ton and the petroleum spreads on the surface of the sea. When an oil spill occurs, there are great concerns to the environment on short term and long term. An oil spill harm ocean life by fouling or oiling, which means that the oil physically harms plants and animals such as coating bird’s wings due to oil and by oil toxicity, which means that the toxic particles cause health problems such as to the immune system and even death.

The impact of an oil spill has large effects and even after a few years, it has proven that there were still high concentrations found in the sea.

To mitigate the effects, new techniques are developed surrounding an oil spill clean-up and one of the most promising technique is the remediation process for reversing environmental impacts. Another way to reduce long-term negative environmental impacts is to clean up oil quickly and efficiently as soon as possible after an oil spill. If there is a new oil skimmer design that is more efficient and takes less time to clean up oil, the long-term impacts will be reduced.

Enterprise

(Oyku) While oil spills continue to represent a serious threat to our ecosystem, governments and organizations throughout the world are investing more in cutting-edge technologies to lessen the dangers associated with them. Our brand-new oil skimmer, which is equipped with Sony XCG-H280CR machine vision camera, is in the leading-edge of this effort. Our skimmer is designed to work in the most extreme environmental conditions, ensuring that oil spills may be contained and cleaned up fast and effectively. It boasts powerful picture processing capability and a durable, waterproof shell. The Sony XCG-H280CR machine vision camera, which enables real-time detection and identification of aquatic waste, is the key component of our skimmer. With a high resolution of 1920 x 1200 pixels and a frame rate of up to 60 frames per second, the camera can take photos quickly and accurately, ensuring that the skimmer operates with the best speed and accuracy possible. Due to its powerful image processing capabilities, the camera can also function well in low light conditions, which can be typical during oil spill cleaning tasks. Our oil skimmer, which we believe to be a significant advancement in technology for cleaning up oil spills, offers organizations and companies looking to lessen the risks associated with oil spills a convincing solution.Whether in response to a spill emergency or as part of routine maintenance tasks, our skimmer is designed to deliver rapid, effective, and environmentally responsible solutions.

Navigational Search Methods

(Eryk)

In literature, there are two search methods that have been implemented in autonomous water/ocean surface operating robots. Deep Reinforcement Learning (CARL-bot) and Coverage Path Planning (SMURF).

Deep reinforcement learning (DRL) is a machine learning technique that has shown great promise in robotics and other complex tasks. One DRL algorithm that has been applied to autonomous oil spill cleaning robots is called V-RACER (Value-based Reinforcement learning for Autonomous Cleaning and Environmental Restoration).

V-RACER is a DRL algorithm that combines deep learning and reinforcement learning to enable the robot to learn how to navigate and clean up an oil spill efficiently. The algorithm uses a neural network to map sensor inputs to actions, allowing the robot to learn how to navigate and clean up the spill in a simulated environment.

In the V-RACER algorithm, the robot is trained in a simulation environment that mimics the real-world conditions of an oil spill. The algorithm uses a reward system to encourage the robot to take actions that lead to the successful cleaning of the spill, such as moving towards areas with higher concentrations of oil and avoiding obstacles.

"CARL-bot is the Caltech Autonomous Reinforcement Learning robot, which is a palm-sized, underwater robot for testing our RL-based navigation in the real world. It will show us how our recent results in a simulated fluid flow hold up for navigating in the real world with all of the associated challenges, such as imperfect sensors and motors, all while training on-board and in real-time"

The algorithm also uses a technique called experience replay, which involves storing and replaying past experiences to help the robot learn from its mistakes and improve its performance over time. The experience replay technique enables the robot to learn from a diverse set of experiences and develop a robust and efficient cleaning strategy.

One advantage of using DRL algorithms such as V-RACER in autonomous oil spill cleaning robots is that they can adapt to different environments and conditions. The algorithm can learn how to navigate and clean up spills in a variety of conditions, such as rough seas, changing currents, and different types of oil.

Coverage path planning is a search strategy commonly used in autonomous systems, including oil spill cleaning robots. The objective of coverage path planning is to ensure that the robot systematically covers the entire area of interest while minimizing the distance traveled and maximizing the cleaning efficiency.

In the context of oil spill cleaning, coverage path planning can be used to guide the robot to cover the entire area of the spill while minimizing the amount of oil left behind. The robot can use various sensors and imaging technologies to detect the extent and location of the spill, and then use coverage path planning algorithms to plan the most efficient cleaning path.

There are several advantages to using coverage path planning in autonomous oil spill cleaning robots. First, it ensures that the entire area of the spill is covered, reducing the risk of leaving oil behind. Second, it minimizes the distance traveled by the robot, reducing the amount of energy consumed and maximizing the battery life. Finally, it ensures that the cleaning process is carried out in a systematic and efficient manner, reducing the time and cost required to clean up the spill.

An existing fully autonomous water surface cleaning robot has been developed using a variation of the coverage path planning method SMURF by a research team in China. It is a trash collection robot that aims to efficiently clean the water surface that it is operating in.

“The environment perception of SMURF relies on the fusion of mmWave radar point clouds and RGB images. SMURF is controlled through our improved NMPC controller to sail along the generated path. When there are obstacles on the path that are detected during the task, a local path will be generated to bypass the obstacles. In addition, the SMURF detects floating trash autonomously and collects the trash in time to avoid trash moving.” (Citation: Zhu, J.; Yang, Y.; Cheng, Y. SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method. J. Mar. Sci. Eng. 2022, 10, 1620. https:// doi.org/10.3390/jmse10111620)

There are various algorithms and techniques that can be used for coverage path planning in autonomous systems. One common technique is the grid-based algorithm, which divides the area of interest into a grid and assigns the robot to cover each cell in a predetermined order. Another technique is the Voronoi diagram-based algorithm, which divides the area of interest into polygons that are covered by the robot in a predetermined order.

In conclusion, the V-RACER algorithm is a powerful DRL technique that has shown great promise in the application of autonomous oil spill cleaning robots. By using a combination of deep learning and reinforcement learning, the algorithm enables the robot to learn how to navigate and clean up spills efficiently, even in challenging and changing conditions. The ability to adapt to different conditions and environments makes DRL algorithms such as V-RACER a valuable tool in the fight against oil spills and other environmental disasters. Additionally coverage path planning is an effective search strategy for autonomous oil spill cleaning robots, as it ensures that the entire area of the spill is covered in a systematic and efficient manner. By using sensors and imaging technologies to detect the extent and location of the spill and applying coverage path planning algorithms, the robot can maximize cleaning efficiency while minimizing energy consumption and time required to clean up the spill.

Coverage Path Planning

Firstly, coverage path planning is defined as:

"... the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles.

https://www.sciencedirect.com/science/article/abs/pii/S092188901300167X#:~:text=Coverage%20Path%20Planning%20(CPP)%20is,of%20interest%20while%20avoiding%20obstacles.

This type of navigational method is very common within autonomous household devices such as vacuum cleaners, lawn mowers and many more.

Coverage path planning is an essential technique for modern-day robots, which involves developing algorithms and strategies to enable robots to navigate through an environment and cover a specified area. Here are some steps to consider when implementing coverage path planning in modern-day robots:

1.    Define the problem: The first step in implementing coverage path planning in modern-day robots is to clearly define the problem you want to solve. You need to determine the area you want the robot to cover, the obstacles in the environment, and the resources available to the robot.

2.    Choose a suitable algorithm: Once you have defined the problem, the next step is to choose a suitable coverage path planning algorithm. There are various algorithms available for this purpose, such as potential fields, cellular decomposition, and Voronoi diagrams. You need to consider the specific requirements of your problem and choose the algorithm that best meets those requirements.

3.    Map the environment: Before the robot can start moving, you need to map the environment. This involves creating a digital map of the area the robot will be operating in. The map should include details such as the location of obstacles, the boundaries of the area to be covered, and any other relevant information.

4.    Plan the path: With the algorithm and map in place, you can now plan the path for the robot. The path should cover the entire area that needs to be covered while avoiding obstacles and using the least amount of resources possible.

5.    Implement the algorithm: Once the path has been planned, it's time to implement the algorithm. This involves programming the robot to follow the path and use the coverage path planning algorithm to avoid obstacles and maximize coverage.

6.    Test and refine: Finally, you need to test the robot and refine the algorithm as necessary. This may involve tweaking the path or adjusting the algorithm to improve performance. With each iteration, the robot should become more efficient and effective at covering the specified area.

Overall, implementing coverage path planning in modern-day robots requires careful planning, algorithm selection, and testing. However, with the right approach, this technique can be a powerful tool for maximizing robot efficiency and productivity in a wide range of applications.

However, CPP works well in static environments for example the inside of a home or the front lawn of house. In a dynamic environment, such as the ocean, environmental factors like wind and currents are huge challenges for CPP based robots. Therefore, the autonomous drum oil skimmer could only be deployed within a bounded area created by floating barriers.

"Therefore, the challenge of the CPP path optimality is to minimize the total travel time along the coverage path and reduce the turning cost."

https://ieeexplore.ieee.org/document/9523743

Hardware

Which camera to use?

(Eryk) The ocean environment presents unique challenges for industrial image recognition systems, as cameras must be able to withstand the harsh conditions of saltwater, strong currents, and changing light conditions.

The hardware used for image processing systems, namely cameras, are distinguished by two groups. The first group being industrial/machine vision (MV) cameras and the second group being network/IP (Internet Protocol) cameras.

Industrial image recognition requires high-resolution cameras capable of capturing clear and detailed images even in low light conditions. High-resolution cameras can capture images up to 4K or 8K, allowing for detailed analysis of underwater images. Remote sensing cameras can capture images from a distance, allowing for long-range monitoring of underwater environments. They are useful for monitoring oceanic currents and weather patterns

(Oyku) The goal of this study is to develop a camera system that can interact with a drum oil skimmer and record photos of debris while simultaneously identifying and ignoring plastic particles that could clog the skimmer. In this situation, using a machine vision camera with picture recognition skills is conceivable. There are numerous machine vision cameras available on the market. There are various considerations that must be made while choosing a camera. Resolution is a crucial feature to consider. To get clear images of the floating waste in the water, the camera's resolution must be high enough. The frame rate is another significant feature. The number of video frames that the camera records each second is known as the frame rate. The camera's frame rate must be high enough for the real-time image processing to function effectively. A decent camera system also needs to be able to process images. The camera should either have built-in image processing hardware or be compatible with external image processing units to enable accurate and effective trash detection. The lighting conditions should also be considered. The camera need should be able to take crisp pictures in both bright and dim lighting conditions. Environmental resilience and strength features are crucial, too. The camera ought to be able to withstand exposure to severe weather, including exposure to moisture, salt, and extreme heat.

(Oyku) For our oil skimmer, we decided on the Sony XCG-H280CR machine vision camera due to its exceptional performance and cutting-edge capabilities. Even in difficult climate conditions, the camera's high resolution of 1920 x 1200 pixels and quick frame rate of up to 60 frames per second enables it to record clear and accurate photographs of the ocean surface. Our skimmer can rapidly identify and remove oil from the water while leaving behind other items thanks to its excellent image processing capabilities, which include real-time debris identification and recognition. In addition, the Sony XCG-H280CR is a resilient camera designed to survive challenging environmental conditions, like those frequently present during oil spill cleanup efforts. While its industrial-grade components guarantee that it can withstand the demands of long-term usage, its waterproof and dustproof housing ensures that it operates reliably in wet and dusty circumstances. Overall, we've found that this camera's vision provides the perfect balance of performance, dependability, and longevity for our oil skimmer. We are certain that this camera will assist our skimmer to perform at its best, offering quick and effective oil spill cleanup services that contribute to the protection of our environment from oil spills.

How to implement this camera system to the oil skimmer?

...

Simulation

Next to the research we conducted, we made a simulation of how a drum skimmer works with and without our proposed improvement. The main goal of these simulations is to be better able to explain how our variation on the skimmer would work. It can also give people who are interested a better idea of why it would be a good improvement, compared to just explaining our idea.

Besides from helping explain to potential users how our proposal would work, the simulation could also help potential users identify problems in the design that we did not see due to our limited knowledge about oil skimmers. The simulation can therefore help us include potential users in the design.

Next to helping potential users better understand how our system would work, the simulations are based on data, in order to show in which environments our system would be an improvement to the effectiveness of the skimmer.

Weekly Work

Logbook
Who?	Eryk	Nika	Matilda	Oyku	Sirii	Maud
Week 1 + hours	Created structure of the report	Thinking about subject and research question (4)		Researching about ocean garbage collectors, and how they work		Researching the current state of the art of autonomous underwater garbage collectors (8)
Week 2 + hours	Problem Statement research	Research to different type of oil skimmers (6)		Research about the types of oil skimmers, and their advantages		Researching how oil skimmers work (6)
Week 3 + hours	Comparison of autonomous drum oil skimmer and non-autonomous drum oil skimmer	Methodology + start problem statement (6)		Getting in contact with oil skimmer companies and continue working on the report		Working on netlogo-model (2)
Week 4 + hours	Naviagtional method for ocean surface robots	Analyzing 3 papers that focuses on the oil recovery rate of drum skimmer / the number of oil spills/ the effects of a droplet oil spill (8)		Research about the camera system that is going to be implemented on the drum oil skimmer		Writing about why we chose to make a simulation (1)
Week 5 + hours
Week 6 + hours
Week 7 + hours
Week 8 + hours

Week 4

Eryk: Comparibility, what is the final goal?

A normal drum oil skimmer and an autonomous drum oil skimmer equipped with plastic recognition software are both designed to clean up oil spills in the ocean, but they differ in their effectiveness and capabilities.

A drum oil skimmer is operated by humans and requires a vessel or platform to be mounted on. It uses a large rotating drum that is partially submerged in the water covered in a material that attracts and collects oil to scoop up the oil from the surface of the water. While normal drum oil skimmers are effective at collecting oil, they do not have the ability to distinguish between oil and other materials, such as plastic waste. This means that they can inadvertently collect debris along with the oil, which can reduce their effectiveness and potentially harm marine life.

Drum oil skimmers can be operated using various power sources, including electricity, hydraulic power, or air power. They are typically mounted on a vessel, such as a boat or barge, which is used to transport the collected oil to shore for disposal.

An autonomous drum oil skimmer equipped with plastic recognition software, on the other hand, is designed to operate without human intervention and is equipped with sensors and software that allow it to identify and avoid debris in the water. This type of skimmer uses machine learning algorithms to analyze images captured by its cameras and distinguish between oil and other materials, such as plastic waste. This means that it can effectively clean up oil spills without inadvertently collecting debris, reducing its environmental impact and protecting marine life.

In terms of effectiveness, an autonomous drum oil skimmer equipped with plastic recognition software is generally more effective than a normal drum oil skimmer. This is because it can operate continuously, even in rough seas or adverse weather conditions, and is able to distinguish between oil and other materials in the water, allowing it to focus solely on the oil spill.

Overall, while normal drum oil skimmers are effective at cleaning up oil spills, an autonomous drum oil skimmer equipped with plastic recognition software represents a major advancement in oil spill cleanup technology and has the potential to significantly improve the effectiveness of oil spill response efforts.

Notes from tutor session:

what is the final goal?

How do you plan to acheieve it?

How do you compare our simulation to a real life?

could compare different algorithms

look at the dependencies of the input parameters

Try to make them realistic based on literature

Then compare the times (Very hard to accept this as a proof)

Ask oil skimming companies if they have

Make explicit what the point is of the simulation

Add the storage capacity of the oil skimmer to make it as real as possible

How do you plan to acheieve it?

How do you compare our simulation to a real life?

could compare different algorithms

look at the dependencies of the input parameters

Try to make them realistic based on literature

Then compare the times (Very hard to accept this as a proof)

Ask oil skimming companies if they have

Make explicit what the point is of the simulation

Add the storage capacity of the oil skimmer to make it as real as possible

Week 3

Tasks

Mathilda:

-Requirements for problem

-Unity Simulation

Eryk

-Report Structure on the Wiki

-Netlogo help

   -How we want the skimmer to behave in the environment (how we want it to move)

-Fluid Dynamic behavior

I tried looking at existing Netlogo models to maybe see if there are any that resemble the type of behavior we want. I found two that may provide a lot of help in developing the environment. The first model called "Membrane Formation" models the interaction of lipid groups surrounded by water molecules. Since lipids are essentially oil, the behavior is exactly what is needed. These are the following advantages and disadvantages to this model:

+Interaction with water and oil

+Links large groups of lipid molecules

-Has a shaking behavior

-No possibility to add a disturbance to the system such as an ocean current

The second model found is called "Slime Mold Network". It demonstrates the growth of a certain fungus and how it is able to expand throughout the environment. Here are the advantages and disadvantages to this model:

+Links with other patches

-Starts its own path

-Ever more expanding

+Very small particles

-Makes paths instead of groups

I believe that the combination of these two models could provide us with a dynamic Netlogo environment that could help us in training our simulation to avoid floating debris in the water.

-Unity Physics behavior

Oyku

-Contact with oil skimming companies

-Report Work

Maud

-Netlogo Simulation

Sirii

-Model the oil skimmer in CAD (NX model)

Nika

-Report Work

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. (Tata, Gautam & Royer, Sarah-Jeanne & Poirion, Olivier Bertrand & Lowe, Jay. (2021). DeepPlastic: A Novel Approach to Detecting Epipelagic Bound Plastic Using Deep Visual Models.)
• 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.(Kylili, K., Kyriakides, I., Artusi, A., & Hadjistassou, C. (2019, April 18). Identifying floating plastic marine debris using a deep learning approach - environmental science and Pollution Research. SpringerLink. Retrieved February 22, 2023, from https://link.springer.com/article/10.1007/s11356-019-05148-4)
• Vito remote sensing: company using satellites and AI vision for multiple applications, including detection of marine plastic litter.(Knaeps, E. (2022, March 17). Artificial intelligence to detect marine plastic litter: Vito Remote Sensing. Prism | Vito remote sensing. Retrieved February 22, 2023, from https://blog.vito.be/remotesensing/ai-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. Effectively 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: when a piece of plastic become stuck in the machine, which could block the machine
  • Effectiveness of different oils: some oil skimmers are more effective for rivers or oceans, but the effectiveness also depends on the type of oil for different oil skimmers.
  • Skimming direction: because most oil skimmers don't use a camera but only sensors it could be hard to find the oil and therefore to do its job

• Existing robots
  • 1. Unmanned floating waste collecting robot(https://ieeexplore-ieee-org.dianus.libr.tue.nl/stamp/stamp.jsp?tp=&arnumber=8929537&tag=1)
    • Collecting floating trash in oceans using a robot hand and collecting it using a belt, which is connected to the bin.
    • Assist humans in removing trash, so it is not autonomous.
    • We could work further on this design to improve the robot by making it autonomous. We could use machine learning that detects plastics in the ocean and therefor collects the plastics or avoid the plastics to solve the problem that the oil skimmer become stuck. In addition, by making the robot autonomous it is possible that the robot works more hours and thus be more effective to collect oil since it disperse quickly in the water.

• 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, how and what could we improve to make it more effective and solve problems
  • How can we make sure that the robot detects the plastics by implementing machine learning?
  • What are the effects for the user by designing an autonomous oil skimmer?
  • What are the effects for the environment / fish?

Eryk:

Since the previous meeting with the tutors, it was made clear that our group should focus on solving a very small issue within a large topic. We want to add to the current research about a topic, which got us thinking about the issue of oil spills in the ocean. Within this topic, we chose to focus on implementing an image recognition algorithm that helps to avoid solid debris on the ocean surface for autonomous oil skimmers.

Initial Research:

https://www.oilskim.com/blog/the-better-belt-skimmer-solving-the-issues-customers-experience-with-traditional-belt-skimmers#:~:text=%E2%80%9CThe%20oil%20skimmer%20belt%20is%20not%20picking%20up%20enough%20oil.&text=Since%20the%20belt%20only%20operates,leading%20to%20poor%20oil%20removal.

Performing a simple google search about the problems concerning oil skimmers, it is quite evident that oil skimmers require a lot of maintenance due to debris becoming stuck in the mechanism. From OilSkim.com, "since the belt only operates on a small section of the tank or pit, debris can build up and form a dam of sorts, preventing oil from reaching the belt. This adversely affects the skimmer’s efficiency, leading to poor oil removal." The skimmer belt often jumps off track due to this debris, which leads to the high maintenance efforts.

In addition to this, the only way to perform maintenance on the oil skimmer is by removing it from the water "...or the tank may need to be drained, which incurs additional downtime and expenses". Stated by Manufacturing.com, " An operator is responsible for ... preventing the lip from being blocked by floating debris... Because overflow weirs require constant supervision, they are not an efficient separation method." Therefore this already creates an incentive for multiple societal entities such as oil companies, oil skimming companies, governments and wildlife conservationists to name a few.

https://www.manufacturing.net/home/whitepaper/13222228/the-challenges-of-removing-surface-freefloating-oil

The efficiency of oil skimmers is calculated using the recovery efficiency definition. The Recover Efficiency percentage (RE%) is defined as the ratio between the volume of oil recovered and the volume of total fluid recovered by the skimmer. Apparently, the RE in ideal conditions ranges between 50% and 85-90%, however we can say that in real conditions it will hardly exceed 50%.

https://sedosr.com/the-problem-of-mechanical-recovery-efficiency-i/

Then the question arises, why would this active avoidance system be better than an additional system that passively sorts the larger debris before collecting oil. Something as simple as a mesh guard has been seen on oil skimmer before however do create problems. Mesh is usually used in these oil collection systems as a drainage system. The way that it works is that as water flows through the mesh, the oil adheres to the mesh as the water just drains away. The oil that collects on the mesh then needs to be scraped manually. Therefore having a mesh to collect debris would actually inhibit the collection of oil from the water surface. Any other way could result in turbulent surface currents which fragment the oil and spread it out more.

https://www.ukpandi.com/media/files/imports/13108/articles/8435-tip-5-use-of-skimmers-in-oil-pollution-response.pdf

The need for our system is present and is reasonable for the duration of our project.

Oyku:

• Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution: https://www.mdpi.com/2077-1312/10/7/925 (page 13)

- We can divide the oil skimmers into 2 categories: oleophilic skimmers(such as disc skimmers) and weir skimmer.

- Authors recommended that disc skimmer should be used for marine pollution (with oils such as gasoline)

- But, disc skimmers are not sensitive to small and solid contaminants such as plastic, so it can just collect them too --> We can focus on implementing our camera system to these kind of skimmers but is it good for the disc skimmer to avoid small solid plastics instead of collecting them unpurposely?

- " The most significant disadvantage of disc skimmers is the weak efficiency in persistent oils such as crude oil and the sensitivity to ropes and river grasses due to constriction and discs"

- I came to the conclusion that most of the oil that is spilled in oceans are marine oil spills.

• Use of Skimmers in Oil Pollution Response:https://www.ukpandi.com/media/files/imports/13108/articles/8435-tip-5-use-of-skimmers-in-oil-pollution-response.pdf

- Limitations of oil recovery: adverse weather conditions, oil viscositiy and effects of currents and waves (nothing is written on this research about plastics)

• Question/concern: I am not sure if it makes sense to develope a camera system which ignores the plastics in the ocean in order to skim the oil in the ocean faster and in a more smooth process. Does it make sense to ignore the plastics on the way?

• Some research about what is oil pollution, where does it come from and what happens to it (we can use these info. in introduction when we are explaining our problem) : https://www.bigblueoceancleanup.org/oil-pollution

- What is it? There are many different types of oils. Because of their wide range of different characteristics, they can have different effects on the ocean, since they have compositional differences.

- Where does it come from? Oils are primary sources of energy and they are used worldwide everyday. That's why daily oil spills also increased a lot. Especially Marine oil spills are the ones that are most concerning. These spills usually occur because of human errors such as equipment failure or illegal dumping.

- What happens to the oil that is spilled? Most of the oil that is spilled stays on the surface of the ocean, and it spreads rapidly by bforming a thinner and thinner layer at the surface of the sea.

- How does it affect humans ? Health: As the oil chemicals are being found in the ocean and inside the fish, as humans being fish eaters it is possible that contaminated fish consumption may have significant consequences in public health.

Economy: " With a high proportion of the world’s population living by and having dependence on the ocean for income, resources and food, the impacts of oil spills are of significant concern socioeconomically. Damage to the environment from oil impacts tourism, industrial and localised fisheries. "

Siiri:

Two options for the prototype:

1.    Full prototype that can move around on the surface of water and collect oil

2.    Simple prototype that only has a camera for testing image recognition

Parts needed for the (full) prototype:

Electronics:

·       Arduino Uno (Arduino Uno R3 - A000066 (tinytronics.nl))

·       Wires, breadboard, resistors etc

·       Battery + battery holder

·       Camera (OV7670 CMOS Camera Module - OV7670 (tinytronics.nl))

·       Pump (for skimming oil) + hose

·       Servo motor S3003 Servo - S3003 (tinytronics.nl)

Other parts:

·       Robot body (3D print)

·       Rotor (Bitcraze Propeller Pack for Crazyflie 2.X - 2x4 pieces - SEEED-110990162 (tinytronics.nl))

·       Pulleys + belt for attaching the rotor (GT2 Pulley - 16 teeth - 5mm axle - GT2PULLEY16T5MM (tinytronics.nl) , GT2 Timing Belt - 6mm - 110mm - Closed - GT2-BELT-110MM-CLOSED (tinytronics.nl))

Things needed for testing the prototype:

·       Bucket of water

·       Plastic

How to use a camera with Arduino (circuit and Arduino code):

How to Use OV7670 Camera Module with Arduino​ Uno (circuitdigest.com)

Week 1

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

References