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

Mandel, Nicolas; Milford, Michael; Gonzalez, Felipé

doi:10.3390/s20164420

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…However, low average precision values for the best performing models (i.e., 17.41% on the VisDrone2019 dataset compared to 40.6% on the MS-COCO dataset) implies that further efforts in CNN architectures for object detection from airborne UAV data are required. Future implementations of CNN models discussed above could require a separate study of any model performance impacts caused by the streaming and processing of high-resolution frames onboard sub-2 kg UAVs, and onboard inference of CNN models in embedded computing systems, such as the UP 2 , Jetson Nano, and Edge TPU [45,46].…”

Section: Discussionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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Recent advances in autonomy of unmanned aerial vehicles (UAVs) have increased their use in remote sensing applications, such as precision agriculture, biosecurity, disaster monitoring, and surveillance. However, onboard UAV cognition capabilities for understanding and interacting in environments with imprecise or partial observations, for objects of interest within complex scenes, are limited, and have not yet been fully investigated. This limitation of onboard decision-making under uncertainty has delegated the motion planning strategy in complex environments to human pilots, which rely on communication subsystems and real-time telemetry from ground control stations. This paper presents a UAV-based autonomous motion planning and object finding system under uncertainty and partial observability in outdoor environments. The proposed system architecture follows a modular design, which allocates most of the computationally intensive tasks to a companion computer onboard the UAV to achieve high-fidelity results in simulated environments. We demonstrate the system with a search and rescue (SAR) case study, where a lost person (victim) in bushland needs to be found using a sub-2 kg quadrotor UAV. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP). A motion strategy (or policy) is obtained once a POMDP is solved mid-flight and in real time using augmented belief trees (ABT) and the TAPIR toolkit. The system’s performance was assessed using three flight modes: (1) mission mode, which follows a survey plan and used here as the baseline motion planner; (2) offboard mode, which runs the POMDP-based planner across the flying area; and (3) hybrid mode, which combines mission and offboard modes for improved coverage in outdoor scenarios. Results suggest the increased cognitive power added by the proposed motion planner and flight modes allow UAVs to collect more accurate victim coordinates compared to the baseline planner. Adding the proposed system to UAVs results in improved robustness against potential false positive readings of detected objects caused by data noise, inaccurate detections, and elevated complexity to navigate in time-critical applications, such as SAR.

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Section: Discussionmentioning

confidence: 99%

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

et al. 2021

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Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing

Haughn,

Harvey,

Inman

2024

Commun Eng

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Uncrewed aerial vehicles are integral to a smart city framework, but the dynamic environments above and within urban settings are dangerous for autonomous flight. Wind gusts caused by the uneven landscape jeopardize safe and effective aircraft operation. Birds rapidly reject gusts by changing their wing shape, but current gust alleviation methods for aircraft still use discrete control surfaces. Additionally, modern gust alleviation controllers challenge small uncrewed aerial vehicle power constraints by relying on extensive sensing networks and computationally expensive modeling. Here we show end-to-end deep reinforcement learning forgoing state inference to efficiently alleviate gusts on a smart material camber-morphing wing. In a series of wind tunnel gust experiments at the University of Michigan, trained controllers reduced gust impact by 84% from on-board pressure signals. Notably, gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six pressure tap signals. By efficiently rejecting environmental perturbations, reduced-sensor fly-by-feel controllers open the door to small uncrewed aerial vehicle missions in cities.

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Incorporating Hierarchical Information for UAV based Semantic Mapping

Mandel

Milford

Gonzalez

2021

2021 IEEE Aerospace Conference (50100)

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

Cited by 6 publications

References 38 publications

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

Deep learning reduces sensor requirements for gust rejection on a small uncrewed aerial vehicle morphing wing

Incorporating Hierarchical Information for UAV based Semantic Mapping

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