The commercial prospects for uncrewed aircraft systems (UAS), also referred to as unmanned aircraft systems, are currently limited by persistent issues around safety. Cracking the conundrum of regulatory obstacles – related to societal acceptance – offers the keys to the future.
We believe that the answer lies in a new generation of automated, collaborative uncrewed aerial vehicle (UAV) fleets with pilots on the loop, not in the loop. In fact, we are sure of it – and we’ve invested in a live working demo to support the thesis.
This Innovation Briefing reveals details of that drone fleet demonstration, exciting prospective commercial use cases and the business opportunities these present for ambitious operators.
The paper focuses on three take-aways:
- A ground-breaking application of autonomy is ready to accelerate the public acceptance and commercial viability of UAVs, transforming safety, reliability and trust
- Advances in drone AI have unlocked a new paradigm of collaborative autonomous UAVs that are able to share tasks and respond to dynamic environments through a dynamic hierarchy of command and control
- The unprecedented levels of teamwork will elevate current commercial applications, enable new levels of drone navigation, and unleash a new generation of novel use cases offering significant business advantage
専門家
Martin specialises in helping international clients realise their ambitions through the business application of transformational new digital services that leverage AI, immersive UX, Web3, cloud computing and ubiquitous networks.
Sam brings decades of experience in system engineering and embedded software design in a variety of wireless technologies, from satellite telephony to personal area networks, and in applications from high-end audio to ultra-low power medical devices. He has an MA in Mathematics and a PhD in Applied Mathematics.
世界初のプッシュ・ツー・トーク式衛星サービスや、低電力の衛星通信技術を使用したUAVの目視外飛行ソリューションなど、社会に変革をもたらし、ビジネス価値を実現するブレークスルーテクノロジーの達成に関して多くの企業を支援している。
Joe specialises in developing real-time AI solutions within the fields of machine vision and robotics. In recent years he has focussed on translating ground-breaking advances in reinforcement learning from academia to a broad range of industrial robotic applications.
Kiran specialises in applying multi-agent reinforcement learning to domains where autonomous agents must learn to achieve complex tasks in a collaborative manner. Her prior work was on applying such methods in the renewable energy space.
