The industry is on the cusp of a significant ramp-up as recent technological breakthroughs promise unlocking of radically futuristic solutions
Microsoft and Hewlett Packard Enterprise are teaming up with NASA to put artificial intelligence (AI) to work on mundane orbital tasks, starting with the chore of checking spacewalkers’ gloves for wear and tear. That’s just one of two dozen experiments in AI, cloud, and edge computing that have been run on HPE’s Spaceborne Computer-2 since the hardware was sent to the space station a year ago. Microsoft aims to bring AI to space and empowering space developers off the planet with Azure, and it’s enabling the ability to build in the cloud and then deploy in space. But this is just the beginning.
AWS introduced Snowcone a couple of years ago as an addition to AWS’ Snow Family. It essentially allows for edge computing and data transfer in disconnected environments. It is a tiny little device, small enough to fit inside the average backpack and light enough to board a space mission without an afterthought. If anything, its presence on the ISS (International Space Station) is the perfect application to showcase the capabilities of such a small and rugged device. The AWS Snowcone provided compute, storage, and networking capabilities on board the ISS and away from any ground facilities, allowing researchers to do their work without the need to transfer data to the ground.
A crowded space creates limitless opportunities
Between 2013 and 2040, 93 satellite operators will be responsible for 43,801 satellites. The actual number of satellites that launch will be much greater; and this is opening up opportunities for NewSpace operators where literally the heavens are the limit, especially around AI and automation. This large influx of small satellite constellations will require automated mission operations. As regulatory frameworks evolve to enhance the safety and fair use of space operations, automation requirements will need to adapt. NewSpace operators are planning for comprehensive mission operations with high levels of automation to establish regulatory compliance while also achieving core mission objectives.
AI in space automation
Satellite operators must automate their mission operations to prioritize safety and efficiency without compromising one for the other. AI capabilities will automate and guide the planning, scheduling, and executing of mission operations tasks (command and control) and will detect/predict anomalies and execute timely remedial measures with limited or no manual input. The ground and space segments will implement AI-driven mission operations. Automated telemetry analysis and suitable software upgradation will remain prominent in the ground segment, while autonomous navigation will be an important enhancement in the space segment. The development and deployment time frame will vary between missions, as AI capabilities, by design, take time to learn from user inputs and historical data. The initial deployment phase will require manual support to correct anomalies.
AI-enabled space services an emerging trend
The NewSpace economy and the subsequent trend of satellite constellations have resulted in an increased need for AI capabilities. Many disruptions, such as deep space missions and automated mission operations, require AI capabilities. The trend has already begun, but in the next 2 years, many NewSpace start-ups will deploy AI in their operations as they expand, and multiple information and communication technology (ICT) market participants will develop customized AI solutions for space industry participants to leverage. AI-enabled space services will become an industry-wide trend, particularly in the downstream and satellite operations areas. The competition is slowly developing in the market and will increase in the next 5 years.
Due to the advent of mega-constellations, AI has gained a lot of interest from industry participants to help them meet the industry’s high-volume demands while reducing costs and increasing efficiency. AI in the space industry will help enable automation across the value chain.
Simulation platforms to test AI deep-space solutions
Solution providers in AI must establish working relationships with NewSpace constellation operators and ground station operators to develop customized AI solutions that integrate with existing mission operations infrastructure. The opportunity is to create task-specific AI solutions so customers can plan and execute AI deployments in phases without risking mission safety. Companies are developing simulation platforms to educate audiences on AI solutions’ benefits and services/products that will enhance their mission planning and command and control.
Participants are carrying out multiple ongoing and planned deep space missions aimed at target locations, including the moon and Mars. Deep space missions require high levels of automated command and control and data processing. These missions will require customized AI-enabled mission planning and command and control. AI deployments will increase as the missions deploy more systems (orbiter/lander/rover). Because the mission operator will not be able to communicate with the spacecraft regularly, such operations will rely on automated command and control to detect, track, and mitigate anomalies.
Different commercial space market participants’ responses to evolving requirements will result in newer iterations of solutions for deep space missions. AI deployments will have to factor in this element of ambiguity and change to manage multiple revisions of mission plans.AI solution developers must establish working relationships with deep space mission operators and evolve customizable AI capabilities that they can embed into the mission planning/execution.
There are huge opportunities in developing simulation platforms, so customers can experiment with AI solutions before making deployment decisions. AI solution providers are working relationships with designers of major deep space mission systems to identify specific operational requirements and constraints; evolve suitable AI solutions matching those requirements and constraints in a modular format, so mission control stakeholders can integrate standalone solutions into a comprehensive solution to further evaluate the respective systems.
Downstream analytics services
Downstream analytics services, especially in the Earth observation domain, are evolving comprehensive domain awareness platforms that can manage large volumes of diverse datasets. Such solutions aim at realizing comprehensive data mines with multiple customized analytics for each application they wish to serve. Automation will be an inherent part of such systems. With more data coming in and a need for near-real-time analytics, the level of automation will only increase.
Such evolutionary automation-controlled data analytics solutions will rely on AI capabilities to execute their processes and develop customized, data-driven, actionable insights for diverse customer groups. Context and Definition Call to Action AI Solution developers must establish working relationships with downstream analytics solution providers and evolve customized AI solutions that enhance their existing services.
Looking forward, this industry is on the cusp of a significant ramp-up. Recent technological breakthroughs have drastically reduced the cost of rocket launches, and by 2030, analysts expect the number of active satellites to increase by several magnitudes. Greater satellite coverage is significant because it could unlock futuristic solutions like drone deliveries, or bring internet access to the world’s underserved.
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