Autonomous ship guidance system combines probabilistic modeling, AI

Autonomous ship guidance system combines probabilistic modeling, AI

Technology News |
By Rich Pell

Addressing the need to reduce human error in ship navigation, which resulted in up to nearly 40% of all marine accidents in 2017, the researchers say they are developing an automated system that instead relies on both data analytics and AI.

“One main intention for autonomous ships is really for the safety purpose,” says Professor Yan Jin, member of the Department of Aerospace and Mechanical Engineering and project lead. “We’re all human and sometimes we make mistakes due to different situations. But if we have an autonomous kind of decision-making computer program, it would constantly make suggestions to humans.”

Knowing the locations of other ships and objects, the system, say the researchers, can predict those ship’s movements and determine their best possible course of action that minimizes the chance of collision. The AI portion of the system uses reinforcement learning – using simulations of different boating scenarios – to teach the computer how to achieve its goal of not hitting another object.

“At first the computer agent doesn’t know anything,” says mechanical engineering PhD student Xiongqing “Vincent” Liu, who was responsible for developing the AI portion of their system. “It has to explore the simulated environment by itself. If the agent collides with the obstacles, then it will receive a negative penalty. But if it reaches the goal, then it receives a very positive reward.”

After running the simulation thousands of times, the agent learns from its past experiences what trajectory to take to avoid a collision – similar to how a human learns.

“From this process, we can demonstrate that, as the agent trains itself, it can generate some intelligence,” says Liu. “And this kind of intelligence is what humans use to make decisions – it’s kind of their intuition. And this kind of human intuition can be learned by a computer agent.”

The AI system alone is not fully error-proof as it relies on the inputted scenarios – major variations on them can cause confusion and lead to a dangerous trajectory. Even extending the AI’s capabilities beyond these programmed scenarios to incorporate any and all possible situations that can occur, say the researchers, there will always be gaps in its knowledge.

To help fill some of these gaps, an analytics model is also used in the researchers’ navigation system. It uses historical boating data going back over 20 years on past ships’ decisions and outcomes to predict what other vessels are going to do.

“You can imagine there’s an infinite number of trajectories that the vessel could take,” says aerospace engineering PhD student Edwin Williams, who developed the analytics model. “But each one of those infinite trajectories has a certain probability of being taken. What my system does is looks at the entire probability of what those trajectories are and then determines the minimum likelihood of where the other vessel is going to be at any given time.”

This, say the researchers, tells them which path has the lowest probability of a collision occurring. But the system relies solely on the quality and amount of data it has. The more specific the data is – for example, what captain was driving the ship – the more accurate the prediction will be.

In addition to helping to marine vessels, say the researchers, the analytics system is in the beginning stages of being applied to air traffic control and space traffic management. In simulations, it has had a 100 percent success rate for avoiding marine collisions. But, just like the AI system, it is limited by the scenarios provided by the data. By using the two systems together, they have an additional layer of security in case an unexpected situation occurs.

“From doing this research we realize that, when you have two kinds of systems, if they’re not consistent, then you need to advise,” says Jin. “If there’s a person there, that’s great. If there is no person there, then you need to devise another approach or algorithm to really understand or resolve this discrepancy. Then, the decision after that resolution is a safer one.”

Next, the researchers hope to continue their work and develop the system even further, and plan on performing a full-scale test using ship maneuver simulators at the Maritime Technology Division of the Monohakobi Technology Institute in Japan.

Related articles:
Robot ship to navigate Pacific in 2019
Intel, Rolls-Royce partner on autonomous shipping
Autonomous ship navigation systems in the making
New equations help autonomous marine robots navigate
Autonomous driving becomes a topic for inland navigation

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