AI in Space Exploration Goes Full Spectrum
While working in the space industry in 2007, I came across a whitepaper discussing the short-term and long-term goals set by national space agencies’ need for the design of intelligent space agents; or more specifically, artificial intelligence in the field of space engineering and space technology. This whitepaper outlined the need for AI as an enabler for future bases on the moon and future manned mission to Mars. The purpose was smart automation. A Mars round trip communication delay would range from 6.5 minutes to as much as 44 minutes approximately, not to mention the 14-day period every Mars synodic period in which no communication was possible. The goal was for space agents to make autonomous decisions without the need for earth-based interaction to make the mission possible and successful. The expected framework was for AI to focus solely on the mission within the exploration component. It was evident that AI was only considered part of the mission itself and not part of the full space value chain spectrum.
Exploration Value Chain
Let’s now fast forward to AI in space exploration today. AI continues to play a major role in space exploration. Everything about the industry requires machine intelligence and assistance to launch, operate, maintain, control, repair and ensure success.
AI applications include:
• Remote sensing and monitoring
• Data analytics
• Reusable launch and manned vehicles
• Asteroid mining
• Remote missions
Mission success relies on sophisticated computer-assisted models, robotics, algorithms, and communications across long distances. For instance, the 2020 NASA Mars mission is expected to integrate three major areas of artificial intelligence.
-Rovers will be equipped with autonomous driving
-Special AI systems will assist the rovers in performing science tasks
-Sophisticated scheduling system will allow the rover to adjust their to ‘do list’ accordingly
But AI doesn’t stop there, cognitive radio technology will increase efficiency in the response time with longer-duration missions, that go beyond Mars and allows satellites to make real-time decisions without waiting for human instruction. As part of its on-going efforts for space exploration, AI continues to revolutionize how we use data in space to assist with in-orbit and deep-space missions.
Commercial Space Value Chain
Now let’s look at the other side of the space industry, the part that most people do not see in the news: the development stage. The commercial space value chain ranges from contractors in enterprises to startups, all providing the software, hardware and service solutions to make these missions and exploration possible.
Like any contractor in the space industry, it’s all about innovation, low cost and best quality to stay competitive. This all starts with digital transformation. While we like to think that most, if not all space companies are completely digital, it is far from the truth. Like any company, digitalization is a process that takes time to implement with the challenge of digital platform integration and costs becoming the key factors. Space organizations must always focus on quality, scheduling, and costs; but today its also creating value for the customer and this is where the digital mindset with AI comes into play. Enhancing the ecosystem of any organization through digitalization leads to better speed and agility, improved integration, leaner models and transparency at every step of the supply & support chain process, all through applied intelligence. AI is now becoming part of the business process, supply chain management, production, and testing. Companies are now also taking note to integrate AI into the core functions of the organization through recruiting, marketing and finance to help build the tools to predict the resources needed to optimize results and compliment production.
AI is all about data science and with large amounts of data, this is fuel for the AI explosion. In the distant past, AI was considered too risky, too expensive, out of reach and only could be found in the future, well the future is now. AI has proven its support capabilities in previous space missions as well as back on Earth with smart voice assistants such as Alexa and Siri. As we explore AI on the business side, we need to focus on one of its basic components called machine learning. Machine learning is based on training through vast amounts of information and determining the right and wrong approach depending on the situation. It is this machine learning that allows AI to focus on the data collected by analyzing and determining insights to efficiency. This will then lead to developing better predictive models to identify problems or evaluating the risk of quality failures in the supply chain within organizations. AI is already integrated with a number of data creation solutions and other technological value adds:
• IoT and active tags. Sensors and transmitters for traceability purposes
• Robotic process automation. New tools aimed at simple, repetitive admin processes
• 3D printing. Prototyping design freedom
• 3D vision. Augmented reality to train operators
• Blockchain. Digitally exchange, validate and certify documents
These tools are already or will be integrated with artificial intelligence to provide the real potential to optimize and create value. Some key areas that we are already seeing this progress in the aerospace industry are on machine learning and visualization OCR to identify processes as they occur in real time, then using predictive modeling to address problems at the earlier stage where it is easier and less costly to fix. Another area, not as critical on the B2B side of the space industry, is open source big data used to understand and gain insights into the customer experience. This will play a larger role as we start to see more space-related services become part of the consumer side, such as the space tourism sector. As the space industry continues to become more digital with new data creation in manufacturing and operational processes, organizations will leverage AI to integrate with other value-added technologies to improve the key areas of customers, operations, and offerings. But as space organizations continue to embrace AI, it is important that they need to understand how to differentiate between using AI to gain insight into their operations versus using AI to develop new products or enhance their value proposition.
New Human Value Chain
Let’s not forget the human element within organizations. As we explore space, we understand the need to use AI for non-human missions due to critical factors such as longevity in space, resources, and capabilities while crewed missions will continue to integrate AI as a critical supporting tool. On the commercial business side, the human element is critical to the success of machine learning. Only through human integration is machine learning possible. The key is interface and interaction research to provide an intuitive communication and collaboration platform to support natural and intuitive engagement. This experience between machines and humans need to incorporate the emotional and creative side of our human element for machines to truly understand our commands and requests in the long run. It will be these experiences that will allow AI to function at its peak to deliver key results.
The digital mindset and AI are driving profound changes and empowering people as it goes full spectrum integrating the exploration, commercial and human sides of the new space spectrum. Organizations will continue to benefit from its disruption in automation, manufacturing, security and human resources; all leading to more competitive solutions for AI in space exploration and benefiting the space economy as a whole for all people.
CMO with Exodus Space and Chief AI & Digital Transformation Strategist with Tinman Kinetics – I leverage digital solutions to integrate the possibilities of artificial intelligence, digital automation and space exploration. A former expat, I now call Colorado home with my wife and dog Danbi. You can find me on Linkedin or follow me on Twitter