FlockAI: A Testing Suite for ML-Driven Drone Applications

Demetris Trihinas,Karlen Avogian,Michalis Agathocleous,Ioannis Katakis

doi:10.3390/fi13120317

Abstract

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment.

Highlights

FlockAI capitalizes on the former by providing a curated set of controllers embedding high-level abstractions in the Python programming language so that drone experiments can be designed just like a set of blueprints are used to architect a large building. These abstractions enable Machine Learning (ML) practitioners to configure drone resource capabilities, enable sensing and communication modules, deploy their ML algorithm(s) that will run on the drone itself and monitor the performance and impact of the ML-driven tasks
FlockAI is deliberately designed to facilitate the rapid experimentation of ML-driven drone applications to derive analytic insights referring to the impact of ML algorithms to a drone’s resources
Scenario 1 examines the overhead of running an ML algorithm for crowd detection vs. the same drone flying without an ML task configured

Summary

Introduction

We are witnessing the extensive deployment of drones in a diverse set of applications, such as rescue missions [1], infrastructure monitoring [2], urban sensing [3], and precision farming [4]. Due to rough terrain and possible ruins after a disaster (e.g., forest fire), it is difficult to timely deploy emergency monitoring facilities. Missions in such areas are dangerous for first responders and volunteers. With the rapid progression of robotics, artificial intelligence and edge computing, drones are equipped with cameras and sensors, and can aid in identifying survivors, pinpoint rescue routes and detect environmental hazards by obtaining a favorable viewpoint for situational assessment [5]

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