dc.contributor.author | Sandino, Juan | |
dc.contributor.author | Maire, Frederic | |
dc.contributor.author | Caccetta, Peter | |
dc.contributor.author | Sanderson, Conrad | |
dc.contributor.author | Gonzalez, Felipe | |
dc.date.accessioned | 2021-11-16T04:28:04Z | |
dc.date.available | 2021-11-16T04:28:04Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2072-4292 | |
dc.identifier.doi | 10.3390/rs13214481 | |
dc.identifier.uri | http://hdl.handle.net/10072/410133 | |
dc.description.abstract | 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. | |
dc.description.peerreviewed | Yes | |
dc.language | en | |
dc.publisher | MDPI AG | |
dc.relation.ispartofpagefrom | 4481 | |
dc.relation.ispartofissue | 21 | |
dc.relation.ispartofjournal | Remote Sensing | |
dc.relation.ispartofvolume | 13 | |
dc.subject.fieldofresearch | Artificial intelligence | |
dc.subject.fieldofresearch | Machine learning | |
dc.subject.fieldofresearch | Photogrammetry and remote sensing | |
dc.subject.fieldofresearch | Field robotics | |
dc.subject.fieldofresearch | Intelligent robotics | |
dc.subject.fieldofresearch | Image processing | |
dc.subject.fieldofresearch | Signal processing | |
dc.subject.fieldofresearch | Autonomous vehicle systems | |
dc.subject.fieldofresearch | Disaster and emergency management | |
dc.subject.fieldofresearch | Computer vision | |
dc.subject.fieldofresearch | Aircraft performance and flight control systems | |
dc.subject.fieldofresearchcode | 4602 | |
dc.subject.fieldofresearchcode | 4611 | |
dc.subject.fieldofresearchcode | 401304 | |
dc.subject.fieldofresearchcode | 400706 | |
dc.subject.fieldofresearchcode | 460205 | |
dc.subject.fieldofresearchcode | 460306 | |
dc.subject.fieldofresearchcode | 400607 | |
dc.subject.fieldofresearchcode | 400703 | |
dc.subject.fieldofresearchcode | 350703 | |
dc.subject.fieldofresearchcode | 460304 | |
dc.subject.fieldofresearchcode | 400103 | |
dc.title | Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Sandino, J; Maire, F; Caccetta, P; Sanderson, C; Gonzalez, F, Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty, Remote Sensing, 13 (21), pp. 4481 | |
dcterms.license | https://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2021-11-16T04:14:13Z | |
dc.description.version | Version of Record (VoR) | |
gro.rights.copyright | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Sanderson, Conrad | |