Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU

TL;DR

This paper introduces MR-iFLY, a modular machine learning framework for small UAS that enables real-time collision avoidance using embedded GPUs, supporting autonomous navigation and potential 6G swarm applications.

Contribution

The paper presents a novel, optimized, modular UAS autonomy framework with advanced depth estimation and collision avoidance tailored for resource-constrained devices.

Findings

Achieved up to 15X speedup over baseline models in vision components

Demonstrated successful flight tests of collision avoidance technology

Argued potential for reducing communication in 6G swarms

Abstract

Interest in unmanned aerial system (UAS) powered solutions for 6G communication networks has grown immensely with the widespread availability of machine learning based autonomy modules and embedded graphical processing units (GPUs). While these technologies have revolutionized the possibilities of UAS solutions, designing an operable, robust autonomy framework for UAS remains a multi-faceted and difficult problem. In this work, we present our novel, modular framework for UAS autonomy, entitled MR-iFLY, and discuss how it may be extended to enable 6G swarm solutions. We begin by detailing the challenges associated with machine learning based UAS autonomy on resource constrained devices. Next, we describe in depth, how MR-iFLY's novel depth estimation and collision avoidance technology meets these challenges. Lastly, we describe the various evaluation criteria we have used to measure performance, show how our optimized machine vision components provide up to 15X speedup over baseline models and present a flight demonstration video of MR-iFLY's vision-based collision avoidance technology. We argue that these empirical results substantiate MR-iFLY as a candidate for use in reducing communication overhead between nodes in 6G communication swarms by providing standalone collision avoidance and navigation capabilities.