What do we mean when we say "drone autonomy?" How are operators utilizing these capabilities operationally, technically, and legally? Multiple presenters will explore how we are defining and utilizing autonomy today to deliver value, where autonomy needs to be in the future, and how we might go about leveraging these applications.
Project Dr-SUIT: Drone Swarm for Unmanned Inspection of Wind Turbines
In March 2021 AIR6 SYSTEMS | AIRBORNE ROBOTICS, Ocean Infinity, Bentley Telecom and University of Portsmouth started to design and develop the Dr-SUIT project, a system-of-systems solution for safe and efficient inspection of offshore wind farms (OWFs). The Dr-SUIT solution combines a swarm of customized drones fitted with complex sensors/cameras underpinned by algorithms designed to provide optimized swarming constellations, efficient operational sequence, and battery health monitoring. The presentation outlines the autonomous interplay between uncrewed vessel, its Launch/Recovery/Recharge Platform (LARRP), the UAV swarm (2 to 6 drones flying at a time) and what it means to enable drone navigation and data-flow for robust and resilient BVLOS offshore operations.
Alex Fraess-Ehrfeld, AIR6 SYSTEMS | AIRBORNE ROBOTICS
Case Study: UAV’s, 5G and Powerline Analytics Brought to Life
Together with AWS’s secure and scalable infrastructure and Optus Telecommunications 5G network, Unleash live is providing the solution to monitor Endeavour Energy network with Autofly and real time A.I. Powerline. With Endeavour energy’s certified Drone pilots capturing high-definition images from the field and apply AI to allow for a rapid analysis of the infrastructure and any potential faults or damage. This presentation will showcase what it meant to create a scalable, low cost and high-quality digital inspection team that allows Endeavor Energy to manage their vast network.
Hanno Blankenstein, Unleash Live
Building Flight Autonomy with AI Building Blocks
Developing, testing, certifying, and deploying autonomous systems is hard. Aerial Autonomous systems have expensive, complex software and hardware that operate in safety-critical environments. The complexity is further amplified because such systems with perception-action loops need some degree of AI capabilities to deal with the unpredictability of the real world, where traditional rules-based software is ill suited. Building and debugging AI across an infinite tail of real-world edge cases is one of the greatest challenges facing aerial autonomy. Learn how to solve this with new approaches based on an AI-first simulation platform and an AI tech-stack purpose built for aerial autonomous systems.
Ashish Kapoor, Microsoft