MAVBench: Micro Aerial Vehicle Benchmarking

Abstract

Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the "MAVBench" framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV's battery consumption by up to 1.8X.

Orbital Edge Computing

“…We propose to leverage early discard, which has been studied for search [45], video indexing [44], and drone video processing [97]. Recent work [8,82] demonstrates the utility of simulation frameworks for edge computing on drones; cote is an analogous utility for the orbital edge. Machine inference accelerators [14,15,24,33] could significantly shorten full-coverage CNP pipeline depths, although some that rely on temporal data redundancy [10] may have limited benefit for devices capturing images at the GTFR.…”

“…On Earth, sensor systems increasingly leverage edge computing by performing sensor-local data processing in lieu of communication to a cloud datacenter [83]. While access to the cloud from the edge can accelerate computing [8], any benefits depend on backhaul network availability. Highdatarate sensors deployed across large geographic environments face a network bottleneck from the sensor to the datacenter as datarate exceeds bandwidth [44,83].…”

Whole-Field Reinforcement Learning: A Fully Autonomous Aerial Scouting Method for Precision Agriculture

“…Compared to human piloted aircraft, UAS are 3× less expensive, achieve better spatial resolution and pose fewer safety risks [ 11 ]. Traditional UAS approaches for scouting involve a flying a grid-based flight pattern, capturing images comprising the entire crop field [ 12 , 13 , 14 ]. To scout a whole field, a UAS is given a set of waypoints (i.e., GPS coordinates) to follow, taking one picture at each waypoint.…”

“…Boubin et al broadened Lin’s compute modeling by capturing environmental factors for UAS [ 17 ]. In-situ AI [ 19 ] and Boroujerdian et al [ 13 ] generalized these approaches via environmental simulation.…”

A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

“…Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in modern society. The past decade has seen an increased use of UAVs, with applications ranging from wildlife and agricultural monitoring [ 1 , 2 , 3 ], over racing [ 4 ], industrial monitoring [ 5 ], packet delivery [ 6 ], and search and rescue [ 7 ] to planetary exploration [ 8 ]. Solutions to computer vision problems, such as object detection [ 9 ], and robotics problems, such as localization [ 10 ], have been developed and disseminated at a rapid pace, with promising results on benchmarks [ 11 ].…”