Demo: FlockAI - A Framework for Rapidly Testing ML-Driven Drone Applications

As drone technology penetrates even more application domains, Machine Learning (ML) is becoming a key driver enabling intelligence in the sky. However, ML Practitioners and Drone Application Operators are faced with several challenges when wanting to test ML-driven drone applications early in the design phase. These include the development and configuration of experiment use-cases over a robotics simulator along with the collection and assessment of desired KPIs which can range from ML algorithm accuracy to drone resource utilization and the impact of "intelligence"to the drone's energy footprint. This demonstration showcases FlockAI, an open and modular by design framework supporting users with the rapid deployment and repeatable testing during the design phase of ML-driven drone applications over the Webots robotics simulator. Through realistic use-cases, the demonstration will show how FlockAI can be used to design drone testbeds with "ready-to-go"drone templates, deploy ML models, configure on-board/remote inference, monitor and export drone resource utilization, network overhead and energy consumption to pinpoint performance inefficiencies and understand if various trade-offs can be exploited.