Ocean Bridge AI
An autopilot powered by Reinforcement Learning AI in order to make the world's fleet of cargo ships more efficient in an effort to reduce emissions produced by such vessels.
Every day, cargo ships travel the seas to fulfill humanity's every desire, from raw materials and fuel, to merchandise and luxury goods, cargo ships, and the literal backbone of the global economy. Such importance comes at a cost of 1 billion metric tons of carbon dioxide produced yearly by the shipping industry. While I originally created this project with the goal of solving the issue of pollution in my area near the Los Angles and San Diego Ports, it has now expanded as an effort to solve the problem globally. The issue of trying to make the shipping industry more green has two issues, one, new technology such as green fuel or ship designs is a long way off and will not arrive until 2050. Solutions such as green fuel or alternative engines would be slow to develop and often lack funding from big corporations. A second issue is that even if these corporations do adopt these solutions, the implementation of these solutions would be very difficult and costly, which means that there needs to be something to cover the time range when these permanent solutions are being developed and tested, which is where my solution comes in.
Oceanbridge Ai is an Ai system designed to tackle the issue of pollution caused by cargo ships by allowing vessels to apply the most efficient routes through environmental data. Using a genetic algorithm, modeled after evolution, meaning that it is able to generate variations of existing paths, determined which paths are optimal and which are not, then further improvements based on the remaining optimal paths.
The System starts by taking environmental data in the form of wave height and length, wind speed and direction, and current direction. These data are easily collected by existing sensors abroad on these cargo ships and other provided weather data.
Then, the system generates a physics simulation using the provided data. Using a unity simulation, the vessel can be represented in the form of a sphere, and the rest of the environment in the form of multiple platforms. The simulation allows the Ai to apply the algorithm and develop the optimal path. Through a physics simulation, the Ai is able to simulate ahead of time in order to predict how the environment would affect the movement of the vessel and how to optimally reduce that impact, which means less energy expanded, limiting pollution as a result.
Once such as path is created, there are two options, the system could either be directed to present the path to the human control or immediately direct the autopilot to apply the optimal path and make adjustments. Once adjustments have bee made, further data will be collected as a result of the changes and more paths can be generated to continue to maintain the efficiency of the vessel.
As a project aimed to reduce our impact on the environment, the project would benefit everyone at some level. More specifically, the focus of this project would be to serve communities living along the coasts of our planet whose homes would soon find themselves under feet of seawater. This would cause many issues. From a huge refugee crisis to the loss of some of the world's biggest cities. More importantly, it could mean the disappearance of entire island states with entire communities suddenly wiped off the map. Island nations such as Fiji, the Maldives, and the Soloman Islands face complete disappearance in the next 80 years. While these islands are often known for being vacation spots, there have been communities of people living there for thousands of years. Being small nation states means that these islands have little say in creating world policies and can't dictate the actions of larger countries. So outside organizations like ours would need to step up and propose solutions to both raise awareness and solve the problem of climate change one step at a time.
My team has a great deal of experience, in planning, designing, and creating projects designed to bring real impact to the world. With past experience designing software and using machine learning, our team is equipped with the technical skills needed for such a complex project. Furthermore, we are a passionate group who have a personal connection to the issue. I have personally volunteered to teach the younger generation about the ocean through sailing, and as an avid sailor myself, I have been able to observe the impacts that our world has had on the ocean. As a member of such a community that lives near the coast, I can say firsthand that these changes happening to the coastlines around the world are a real problem. From oil spills to toxic gasses, I have first-hand experience with the impacts of cargo ships on communities. We believe that we can deliver this solution because of not only our experience and connection to the environment, and also our drive to do something before it is too late. By both understandings how these vessels function, and what needs to happen, our team is uniquely prepared to tackle this challenge,
In order to understand the true needs of the communities I am serving, I did some research and interviews directly with people impacted every day by the effects of the shipping industry. Moreover, I also research how my solution would work in the grand scheme of the shipping industry. By doing research on the issue, it has become apparent to me that cargo ships are a huge issue for both the environment but also everyday people's health. Cargo Ships create 1 billion metric tons of carbon dioxide per year, in addition to the Release of sulfur dioxide, nitrogen oxide, and more toxic gasses. This release can cause severe health effects to those who live near ports or live in an area with concentrated water traffic. Moreover, cargo ships also release a large amount of oil waste and other toxic water waste. through my volunteering experience, I have learned that while solutions to make cargo ships green are being developed, it would be hard to convince all the companies to adopt such solutions and invest in such solutions. By engaging with exporting in these fields, I have learned that a middle step would be needed before the adoption of these solutions, Where cargo ships could be upgraded to be more efficient to give more time for full solutions to be developed. lastly, by having a conservation with people who are directly impacted by the rising sea level, I have come to understand the need for urgency for this solution and the need for change.
- Taking action to combat climate change and its impacts (Sustainability)
- Prototype: A venture or organization building and testing its product, service, or business model
My solution innovates on the common idea of sustainability through how it is implemented and fits in the big picture of sustainability. Project Ocean bridge is not meant to be a final solution to the issue of cargo ship pollution, but to provide a "bridge" to the final solution. In my opinion, the problem with traditional activism is that a solution to an environmental problem needs to immediately replace traditional technology. Instead, Ocean Bridge Ai allows making existing technology more efficient with the little required physical upgrade and little change to existing practices in the industry. By using this connection with large companies and creating this conversation of sustainability, we hope that we can increase both investments and support for a final solution to the issue of sustainability in the shipping industry. we expect that the solution would have an immediate impact on how people think about cargo ships, both by reducing pollution and by creating a conversation of innovation regarding these vessels. What makes out solution innovative is that while it makes cargo ships sustainable, it also makes them more efficient, which encourages adoption.
For our impact goal of the new year, we aim to do two important things for our project. One is to release our application to public use, meaning any company involved in shipping or operating any type of vessel at a large scale that impacts the environment. Second, we plan to establish more solid relations with industry leaders in the field of shipping and oceanic navigation. In order to do these things, we would develop our solutions to a point where a large impact could be found in our local community. The thing is, while one solution would not be able completely to solve the problem of pollution as a whole, we plan to reduce the pollution of ships using our solution by at least 30%. To meet this target, there would need to be first the introduction of our technology and then implementation, which is our two goals were set as such.
The core technology of our solution is a Machine learning algorithm paired with physics simulation. By combing these two techniques, we were able to truly unlock a new possible use of machine learning in the field of navigation. Obviously, the use of machine learning, physics simulation, and even a combination of both definitely are not new ideas and we don't claim that. However, using machine learning, or genetic algorithm on a physics simulation in order to improve the efficiency of a vessel, we were able to quickly manage a large number of environmental data and calculate at an equally quick pace the most optimal path in terms of efficiency and fuel use. The machine learning algorithm, or genetic algorithm, operates by first generating a random path on the physics simulation, then by improving the path through the model of natural evolution, the path is able to slowly make up for its initial shortcomings and reach the optimal performance in a certain environmental condition. Although usually, fluid dynamics and wind influence would be difficult to calculate, by combining all influences on a ship into simple planes, we are able to reduce calculation time by 10-fold. By using the efficient model of machine learning and physics simulation, the solution is able to be competitive in the market and easy to operate, being able to run on its own without assistance.
- Artificial Intelligence / Machine Learning
- Imaging and Sensor Technology
- Software and Mobile Applications
- United States
Well, our solution is still in its prototype stage so we are currently serving 0 people, and we plan to expand our service to at least two vessels in the next year. this means we will be doing further testing, improving, and innovating as our product is being used. during the trial period. Due to the nature of our product being one that promotes sustainability, it would be hard to know how many people we would benefit from, but with every ton of carbon we prevent from being released, everyone on earth is benefits.
We have many barriers currently, but the three restrictions are a lack of funding, technology, and market access. Due to the complexity of this project, to be able to test it efficiently requires physical testing on an actual vessel, whether it is a medium vessel or cargo ship, we simply do not have that access at this time. This is a problem for us both finically and in terms of available technology for us. To solve both of these problems, we would need to start establishing connections in the market, currently, although we have some connections, they are simply not enough for us to get off the prototype stage and into actual trials.
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The business of our project is focused on the shipping industry. In terms of value, we could save each vessel 20-30 percent of fuel use each trip. Our service is easy to install and easy to use. A simple internet install would allow for automated setup of the software and integration into the system. While other solutions focus mainly on sustainability, our innovative solution focuses on both sustainability and financial efficiency. By saving fuel, shipping companies can cut costs by a significant margin. The easy setup and lack of training needed for this solution would mean that it would be a seamless transition and integration, making it one of the most viable solutions in terms of making the industry much more sustainable.
At first, we would mostly rely on grants and donations in the first stages of our project, however, due to the low-cost nature of our solution, revenue from clients in the industry would be able to sustain us in the future to continue to innovate. Once a service is deployed, it would likely no longer need maintenance from us outside of upgrades. meaning our expenses in that area would be low. Our main source of revenue at the end of the day would be through our services. The business would allow us to further our goals of sustainability and create new solutions through the integrated business model.