Featured Image Source: IBM
The Mayflower Autonomous Ship Project (MAS400) is a collaboration between ProMare and IBM Research. The project is also affiliated with the Submergence Group, A U.K. company that “designs and manufactures manned and unmanned submersibles.” (2)
In a recent press release, IBM provided the following description of the fully autonomous marine vessel with an “AI Captain”:
“In a voyage lasting 40 days and conquering approximately 3,500 unmanned miles at sea, the Mayflower Autonomous Ship arrived in North America in Halifax, Nova Scotia on June 5, 2022. Following two years of design, construction, and AI model training, the Mayflower Autonomous Ship (MAS) was officially launched in September 2020. Fast forward to today, June 6, 2022, we celebrate the completion of MAS’s historic transatlantic voyage from Plymouth, UK to its North American arrival in Halifax, Nova Scotia yesterday, June 5. With no human captain or onboard crew, MAS is the first self-directed autonomous ship with technology that is scalable and extendible to traverse the Atlantic Ocean.” (1)
Fast Company reports that the MAS400 was “originally headed to Washington, D.C., the ship—which is propelled by a solar-driven hybrid electric motor and backup diesel generator, and guided by artificial intelligence, cloud, and edge computing technologies—diverted to Canada last week so the team could fix a faulty generator starter. Later this month, it will continue to Plymouth, Massachusetts, where the first Mayflower landed in 1620, before arriving in D.C. in July.” (2)
MAS400: Project Scope
Fast Company breaks down the project with the following metrics and insights:
- A multicultural team traversing 10 countries, three continents, and four-dozen business and academic partners.
- The 10,000-lb., 50 x 20-foot vessel advances established automated, remote-controlled, and preprogrammed missions by making real-time decisions at sea with no human intervention (though humans can override in emergencies).
- The boat avoids hazards, assesses vehicle performance, replans routes, and copes with other novel situations all on its own.
- The Mayflower also conducted an array of environmental science in remote parts of the ocean. Its findings will help scientists gauge the impact of global warming and pollution on marine life, such as water acidification, microplastics, and mammal conservation.
- Its success may pave the way for flexible and cost-efficient fleets with low carbon footprints gathering ocean data, while its software could be leveraged to manned ships to reduce risks and human error.
- Indirectly, MAS findings could aid the development of autonomous AI systems and augmented intelligence for humans across other industries such as shipping, oil and gas, telecommunications, security and defense, finance, and aquaculture. (2)
MAS400: Design and Technology
Taking the human factor out of the Mayflower has allowed us to completely reimagine the design. Instead of thinking about eating, sleeping, and sanitation, the Mayflower’s engineers were able to focus purely on the mechanics and function of the ship. (Source: Mayflower Autonomous Ship)
“The main challenge with the Mayflower design was configuring the technology to provide the continuous autonomous data necessary for the ship to immediately react. ‘It’s loaded for bear,’ [MAS400 managing director Brett] Phaneuf says with a laugh, alluding to the suite of instruments. The Mayflower sports:
- six AI-powered cameras and more than 30 sensors covering three weather stations, technology for science experiments, and a visualization system to recognize obstacles like standup paddleboarders, other ships, and icebergs.
- [Sensors] include radar, sonar, LIDAR, GPS to within centimeters of accuracy, stabilized 360-degree day and night cameras, thermal imagers, and gauges for motion, fuel, wind, wave height and pattern, and aquatic chemistry.
- That information feeds into the AI Captain, which uses IBM’s Operational Decision Manager decision-making software to guide navigation and analysis, amounting to a grand experiment in machine learning.” (2)
Machine learning: Over the past two years, the Mayflower team has trained the ship’s AI models using over a million nautical images collected from cameras in the Plymouth Sound in the UK as well as open-source databases. To meet the processing demands of machine learning, the team used an IBM Power AC922 fuelled by IBM Power9 CPUs and NVIDIA V100 Tensor Core GPUs, the same technologies behind the world’s smartest AI supercomputers. Now, using IBM’s computer vision technology, the Mayflower’s AI Captain should be able to independently detect and classify ships, buoys, and other hazards such as land, breakwaters, and debris.
Ship Design: The Mayflower team opted for a trimaran design giving it a low, highly stable and dynamic profile. Made from aluminum and composite materials, the Mayflower is very lightweight: about 5 tonnes at 15 meters long and 6.2 meters wide. That’s half the length and less than 3 percent of the weight of the original Mayflower.
Edge computing systems: As MAS will not always have connectivity in the middle of the ocean, it uses a fully autonomous IBM edge computing system powered by several onboard NVIDIA Jetson AGX Xavier devices. While at sea, MAS will process data locally, increasing the speed of decision-making and reducing the amount of data flow and storage on the ship.
Power supply: Lithium ion-phosphate batteries, in addition to solar panels on the ship’s exterior, provide power to the computer systems onboard in addition to supplying energy to the motors for propulsion.
Cargo bay: The modular cargo bay can hold ocean scientific equipment up to 1000KG.
Dual motors: Dual 20 kW permanent magnet electric propulsion motors help to propel the ship at nearly double the speed of the original Mayflower, while producing less carbon than traditional diesel-burning engines. (3)
The Competition
Fast Company provided the following overview of competing projects on the high seas: “While the Mayflower tackled the Atlantic Ocean, a number of autonomous long-haul experiments involving research, commercial, and military ships recently succeeded in the Pacific. Among them, Leidos’ Sea Hunter completed a 5000-mile round trip between San Diego and Hawaii in 2019 as part of a US Navy project; the Saildrone Surveyor research vessel last year finished a 2250-mile journey from San Francisco to Hawaii; and just last week, the Hyundai Heavy Industries merchant vessel, Prism Courage, achieved a 6200-mile trip from the U.S. to South Korea using autonomous navigation for half its voyage.” (2)
What Next?
Autonomous trucking fleets and, now, autonomous marine vessels and fleets, are a better subsector of autonomous capabilities to track (over, say, the adoption rate of consumer class vehicles with autonomous capabilities). Why? The accident rate in the autonomous vehicles subsector is increasingly problematic. Truck fleets and marine vessels, with routes that are less prone to accidents or potential fatalities, will have a clear path (or voyage) for experimentation, interoperability, and scalability. Iterative market results and “killer apps” will emerge more readily in this space, which as the MAS400 team points out: “Indirectly, MAS findings could aid the development of autonomous AI systems and augmented intelligence for humans across other industries such as shipping, oil and gas, telecommunications, security and defense, finance, and aquaculture.”
Consumer autonomous vehicles at scale, driving autonomously on the installed roads and highways infrastructure, has many obstacles to overcome that will impede successful deployment. Follow the trends in autonomous fleets and autonomous capabilities in a wide range of industry sectors. The future of the car is not the only future trend in autonomy, AI, and automation innovation.
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