INSUBCONTINENT EXCLUSIVE:
The rapid growth of autonomous systems in defense and other sectors is creating new challenges in software deployment and management
Market analysis highlights this trend, with the global military drone and unmanned aerial vehicle (UAV) market projected to expand at an
broader autonomous systems market, underscores the urgent need for scalable, secure, and efficient software deployment solutions
As the number of autonomous platforms increases, so does the complexity of managing their software systems
In response to this growing challenge, Shield AI and Amazon Web Services (AWS) have collaborated and successfully demonstrated a scalable
architecture and proof of concept (PoC) that addresses challenges often seen when deploying autonomy software and machine learning
line interface (CLI) or user interface.For defense customers fielding autonomous systems, software deployment challenges stem from several
factors, including:
The expanding variety of autonomous vehicles across air, land, and seaThe increasing need for mission-specific
collaboration between vehiclesThe need to rapidly deploy updates across large fleets of vehicles in Denied, Disrupted, Intermittent, and
Limited (DDIL) environments.
Emerging conflict demands quick software updates and reconfiguration across the various software components of
From embedded vehicle control firmware to mission specific ML models and AI pilots, these updates must deploy securely and reliably across
vehicle fleets maintaining system integrity to counter evolving threats and mission needs.While we have seen rapid development across many
aspects of autonomous vehicle systems including advanced hardware and sensors, autonomous navigation and perception, and communication,
updating these systems often involves manual time-intensive processes that can slow initial development and testing of new capabilities, can
cause bottlenecks during missions, and introduces opportunity for misconfiguration.Liz Martin, AWS managing director, Department of Defense
readiness and address emerging mission requirements
Through the collaboration between Shield AI and AWS, we have a solution that provides scalable development and deployment of mission
AI software stack (EdgeOS) running on autonomous hardware platforms and Shield AI autonomy development platform running in the cloud.Because
the Hivemind Pilots are deployed as containers, once a new deployment is ready and communicated via AWS IoT Core to the autonomous platform,
the container is securely retrieved from Amazon ECR where it is staged by Hivemind as a part of the development and deployment integration,
creating a streamlined cloud to autonomous platform deployment pipeline
In summary, the solution enables:
Centralized control and rapid deployment of Hivemind Pilot softwareSecure, cloud-based distribution to
defense and commercial customers
Defense customers can now maintain heightened mission readiness by rapidly updating their autonomous fleets to counter emerging threats and
adapt to changing mission parameters
This capability is coupled with increased operational flexibility, allowing teams to quickly modify autonomous capabilities for new
environments and mission types
The solution also strengthens security measures by ensuring all platforms consistently run the most current, secure software versions, while
providing centralized management and monitoring of autonomous platforms.In the commercial sector, the benefits extend across multiple
Agricultural operations can now efficiently update fleets of autonomous tractors to accommodate seasonal changes and implement new farming
The oil and gas industry can deploy crucial updates to offshore autonomous systems, enhancing both safety protocols and operational
efficiency.In logistics and warehousing, businesses can seamlessly modify their autonomous vehicle fleets to adapt to evolving inventory
requirements and routing needs
Mining operations benefit from the ability to quickly update autonomous equipment to accommodate new terrain conditions or extraction
methodologies.
Conclusion
The collaboration between Shield AI and AWS addresses a critical challenge in autonomous systems management
fully leverage the power of their autonomous fleets
This integration also establishes a foundation for post-mission data capture, which is crucial for driving rapid pilot development and
continuous improvement of autonomous capabilities.In the next phase of our collaboration, we are transitioning from proof of concept to
