Blue Origin’s New Glenn rocket landed its booster on a barge at sea – an achievement that will broaden the commercial spaceflight market

Wendy Whitman Cobb, Air University Blue Origin’s New Glenn rocket successfully made its way to orbit for the second time on Nov. 13, 2025. Although the second launch is never as flashy as the first, this mission is still significant in several ways. For one, it launched a pair of NASA spacecraft named ESCAPADE, which are headed to Mars orbit to study that planet’s magnetic environment and atmosphere. The twin spacecraft will first travel to a Lagrange point, a place where the gravity between Earth, the Moon and the Sun balances. The ESCAPADE spacecraft will remain there until Mars is in better alignment to travel to. And two, importantly for Blue Origin, New Glenn’s first stage booster successfully returned to Earth and landed on a barge at sea. This landing allows the booster to be reused, substantially reducing the cost to get to space.

As a space policy expert, I see this launch as a positive development for the commercial space industry. Even though SpaceX has pioneered this form of launch and reuse, New Glenn’s capabilities are just as important.

New Glenn in context

Although Blue Origin would seem to be following in SpaceX’s footsteps with New Glenn, there are significant differences between the two companies and their rockets. For most launches today, the rocket consists of several parts. The first stage helps propel the rocket and its spacecraft toward space and then drops away when its fuel is used up. A second stage then takes over, propelling the payload all the way to orbit. While both New Glenn and Falcon Heavy, SpaceX’s most powerful rocket currently available, are partially reusable, New Glenn is taller, more powerful and can carry a greater amount of payload to orbit. Blue Origin plans to use New Glenn for a variety of missions for customers such as NASA, Amazon and others. These will include missions to Earth’s orbit and eventually to the Moon to support Blue Origin’s own lunar and space exploration goals, as well as NASA’s. NASA’s Artemis program, which endeavors to return humans to the Moon, is where New Glenn may become important. In the past several months, several space policy leaders, as well as NASA officials, have expressed concern that Artemis is progressing too slowly. If Artemis stagnates, China may have the opportunity to leap ahead and beat NASA and its partners to the lunar south pole. These concerns stem from problems with two rockets that could potentially bring Americans back to the Moon: the space launch system and SpaceX’s Starship. NASA’s space launch system, which will launch astronauts on its Orion crew vehicle, has been criticized as too complex and costly. SpaceX’s Starship is important because NASA plans to use it to land humans on the Moon during the Artemis III mission. But its development has been much slower than anticipated. In response, Blue Origin has detailed some of its lunar exploration plans. They will begin with the launch of its uncrewed lunar lander, Blue Moon, early next year. The company is also developing a crewed version of Blue Moon that it will use on the Artemis V mission, the planned third lunar landing of humans. Blue Origin officials have said they are in discussions with NASA over how they might help accelerate the Artemis program.

New Glenn’s significance

New Glenn’s booster landing makes this most recent launch quite significant for the company. While it took SpaceX several tries to land its first booster, Blue Origin has achieved this feat on only the second try. Landing the boosters – and, more importantly, reusing them – has been key to reducing the cost to get to space for SpaceX, as well as others such as Rocket Lab. That two commercial space companies now have orbital rockets that can be partially reused shows that SpaceX’s success was no fluke. With this accomplishment, Blue Origin has been able to build on its previous experience and success with its suborbital rocket, New Shepard. Launching from Blue Origin facilities in Texas since 2015, New Shepard has taken people and cargo to the edge of space, before returning to its launch site under its own power.

New Glenn is also significant for the larger commercial space industry and U.S. space capabilities. It represents real competition for SpaceX, especially its Starship rocket. It also provides more launch options for NASA, the U.S. government and other commercial customers, reducing reliance on SpaceX or any other launch company. In the meantime, Blue Origin is looking to build on the success of New Glenn’s launch and its booster landing. New Glenn will next launch Blue Origin’s Blue Moon uncrewed lander in early 2026. This second successful New Glenn launch will also contribute to the rocket’s certification for national security space launches. This accomplishment will allow the company to compete for contracts to launch sensitive reconnaissance and defense satellites for the U.S. government. Blue Origin will also need to increase its number of launches and reduce the time between them to compete with SpaceX. SpaceX is on pace for between 165 and 170 launches in 2025 alone. While Blue Origin may not be able to achieve that remarkable cadence, to truly build on New Glenn’s success it will need to show it can scale up its launch operations. Wendy Whitman Cobb, Professor of Strategy and Security Studies, Air University This article is republished from The Conversation under a Creative Commons license. Read the original article.

AI Spacecraft Propulsion: Machine Learning’s Role in Space Travel

Marcos Fernandez Tous, University of North Dakota; Preeti Nair, University of North Dakota; Sai Susmitha Guddanti, University of North Dakota, and Sreejith Vidhyadharan Nair, University of North Dakota Every year, companies and space agencies launch hundreds of rockets into space – and that number is set to grow dramatically with ambitious missions to the Moon, Mars and beyond. But these dreams hinge on one critical challenge: propulsion – the methods used to push rockets and spacecraft forward. To make interplanetary travel faster, safer and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is one type of technology that has begun to provide some of these necessary breakthroughs. We’re a team of engineers and graduate students who are studying how AI in general, and a subset of AI called machine learning in particular, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind’s journey to the stars.

Machine learning and reinforcement learning

Machine learning is a branch of AI that identifies patterns in data that it has not explicitly been trained on. It is a vast field with its own branches, with a lot of applications. Each branch emulates intelligence in different ways: by recognizing patterns, parsing and generating language, or learning from experience. This last subset in particular, commonly known as reinforcement learning, teaches machines to perform their tasks by rating their performance, enabling them to continuously improve through experience. As a simple example, imagine a chess player. The player does not calculate every move but rather recognizes patterns from playing a thousand matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale impossible for humans. It learns through experiences and iterations by observing its environment. These observations allows the machine to correctly interpret each outcome and deploy the best strategies for the system to reach its goal. Reinforcement learning can improve human understanding of deeply complex systems – those that challenge the limits of human intuition. It can help determine the most efficient trajectory for a spacecraft heading anywhere in space, and it does so by optimizing the propulsion necessary to send the craft there. It can also potentially design better propulsion systems, from selecting the best materials to coming up with configurations that transfer heat between parts in the engine more efficiently.

Reinforcement learning for propulsion systems

In regard to space propulsion, reinforcement learning generally falls into two categories: those that assist during the design phase – when engineers define mission needs and system capabilities – and those that support real-time operation once the spacecraft is in flight. Among the most exotic and promising propulsion concepts is nuclear propulsion, which harnesses the same forces that power atomic bombs and fuel the Sun: nuclear fission and nuclear fusion. Fission works by splitting heavy atoms such as uranium or plutonium to release energy – a principle used in most terrestrial nuclear reactors. Fusion, on the other hand, merges lighter atoms such as hydrogen to produce even more energy, though it requires far more extreme conditions to initiate.

Fission is a more mature technology that has been tested in some space propulsion prototypes. It has even been used in space in the form of radioisotope thermoelectric generators, like those that powered the Voyager probes. But fusion remains a tantalizing frontier. Nuclear thermal propulsion could one day take spacecraft to Mars and beyond at a lower cost than that of simply burning fuel. It would get a craft there faster than electric propulsion, which uses a heated gas made of charged particles called plasma. Unlike these systems, nuclear propulsion relies on heat generated from atomic reactions. That heat is transferred to a propellant, typically hydrogen, which expands and exits through a nozzle to produce thrust and shoot the craft forward. So how can reinforcement learning help engineers develop and operate these powerful technologies? Let’s begin with design.

Reinforcement learning’s role in design

Early nuclear thermal propulsion designs from the 1960s, such as those in NASA’s NERVA program, used solid uranium fuel molded into prism-shaped blocks. Since then, engineers have explored alternative configurations – from beds of ceramic pebbles to grooved rings with intricate channels.

Why has there been so much experimentation? Because the more efficiently a reactor can transfer heat from the fuel to the hydrogen, the more thrust it generates. This area is where reinforcement learning has proved to be essential. Optimizing the geometry and heat flow between fuel and propellant is a complex problem, involving countless variables – from the material properties to the amount of hydrogen that flows across the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer. Imagine it as a smart thermostat but for a rocket engine – one you definitely don’t want to stand too close to, given the extreme temperatures involved.

Reinforcement learning and fusion technology

Reinforcement learning also plays a key role in developing nuclear fusion technology. Large-scale experiments such as the JT-60SA tokamak in Japan are pushing the boundaries of fusion energy, but their massive size makes them impractical for spaceflight. That’s why researchers are exploring compact designs such as polywells. These exotic devices look like hollow cubes, about a few inches across, and they confine plasma in magnetic fields to create the conditions necessary for fusion. Controlling magnetic fields within a polywell is no small feat. The magnetic fields must be strong enough to keep hydrogen atoms bouncing around until they fuse – a process that demands immense energy to start but can become self-sustaining once underway. Overcoming this challenge is necessary for scaling this technology for nuclear thermal propulsion.

Reinforcement learning and energy generation

However, reinforcement learning’s role doesn’t end with design. It can help manage fuel consumption – a critical task for missions that must adapt on the fly. In today’s space industry, there’s growing interest in spacecraft that can serve different roles depending on the mission’s needs and how they adapt to priority changes through time. Military applications, for instance, must respond rapidly to shifting geopolitical scenarios. An example of a technology adapted to fast changes is Lockheed Martin’s LM400 satellite, which has varied capabilities such as missile warning or remote sensing. But this flexibility introduces uncertainty. How much fuel will a mission require? And when will it need it? Reinforcement learning can help with these calculations. From bicycles to rockets, learning through experience – whether human or machine – is shaping the future of space exploration. As scientists push the boundaries of propulsion and intelligence, AI is playing a growing role in space travel. It may help scientists explore within and beyond our solar system and open the gates for new discoveries. Marcos Fernandez Tous, Assistant Professor of Space Studies, University of North Dakota; Preeti Nair, Master’s Student in Aerospace Sciences, University of North Dakota; Sai Susmitha Guddanti, Ph.D. Student in Aerospace Sciences, University of North Dakota, and Sreejith Vidhyadharan Nair, Research Assistant Professor of Aviation, University of North Dakota This article is republished from The Conversation under a Creative Commons license. Read the original article.