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

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 wit...

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Autores principales:	Juan Sandino, Frederic Maire, Peter Caccetta, Conrad Sanderson, Felipe Gonzalez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unmanned aerial system (UAS) unmanned aerial vehicle (UAV) artificial intelligence (AI) embedded systems machine learning (ML) search and rescue (SAR) Science Q
Acceso en línea:	https://doaj.org/article/b6b6c48f97534b51aaf2127e17f5355f
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spelling	oai:doaj.org-article:b6b6c48f97534b51aaf2127e17f5355f2021-11-11T18:58:46ZDrone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty10.3390/rs132144812072-4292https://doaj.org/article/b6b6c48f97534b51aaf2127e17f5355f2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4481https://doaj.org/toc/2072-4292Recent 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.Juan SandinoFrederic MairePeter CaccettaConrad SandersonFelipe GonzalezMDPI AGarticleunmanned aerial system (UAS)unmanned aerial vehicle (UAV)artificial intelligence (AI)embedded systemsmachine learning (ML)search and rescue (SAR)ScienceQENRemote Sensing, Vol 13, Iss 4481, p 4481 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unmanned aerial system (UAS) unmanned aerial vehicle (UAV) artificial intelligence (AI) embedded systems machine learning (ML) search and rescue (SAR) Science Q
spellingShingle	unmanned aerial system (UAS) unmanned aerial vehicle (UAV) artificial intelligence (AI) embedded systems machine learning (ML) search and rescue (SAR) Science Q Juan Sandino Frederic Maire Peter Caccetta Conrad Sanderson Felipe Gonzalez Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
description	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.
format	article
author	Juan Sandino Frederic Maire Peter Caccetta Conrad Sanderson Felipe Gonzalez
author_facet	Juan Sandino Frederic Maire Peter Caccetta Conrad Sanderson Felipe Gonzalez
author_sort	Juan Sandino
title	Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
title_short	Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
title_full	Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
title_fullStr	Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
title_full_unstemmed	Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
title_sort	drone-based autonomous motion planning system for outdoor environments under object detection uncertainty
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b6b6c48f97534b51aaf2127e17f5355f
work_keys_str_mv	AT juansandino dronebasedautonomousmotionplanningsystemforoutdoorenvironmentsunderobjectdetectionuncertainty AT fredericmaire dronebasedautonomousmotionplanningsystemforoutdoorenvironmentsunderobjectdetectionuncertainty AT petercaccetta dronebasedautonomousmotionplanningsystemforoutdoorenvironmentsunderobjectdetectionuncertainty AT conradsanderson dronebasedautonomousmotionplanningsystemforoutdoorenvironmentsunderobjectdetectionuncertainty AT felipegonzalez dronebasedautonomousmotionplanningsystemforoutdoorenvironmentsunderobjectdetectionuncertainty
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Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty

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