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Article

UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments

1
School of Electrical Engineering and Robotics, Queensland University of Technology (QUT), 2 George Street, Brisbane City, QLD 4000, Australia
2
Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Building 101, Clunies Ross Street, Black Mountain, ACT 2601, Australia
3
QUT Centre for Robotics (QCR), Queensland University of Technology (QUT), Level 11, S Block, 2 George Street, Brisbane City, QLD 4000, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3386; https://doi.org/10.3390/rs12203386
Received: 7 September 2020 / Revised: 10 October 2020 / Accepted: 12 October 2020 / Published: 16 October 2020
Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. These efforts have been further improved with advances in autonomous behaviours such as obstacle avoidance, take-off, landing, hovering and waypoint flight modes. However, most UAVs lack autonomous decision making for navigating in complex environments. This limitation creates a reliance on ground control stations to UAVs and, therefore, on their communication systems. The challenge is even more complex in indoor flight operations, where the strength of the Global Navigation Satellite System (GNSS) signals is absent or weak and compromises aircraft behaviour. This paper proposes a UAV framework for autonomous navigation to address uncertainty and partial observability from imperfect sensor readings in cluttered indoor scenarios. The framework design allocates the computing processes onboard the flight controller and companion computer of the UAV, allowing it to explore dangerous indoor areas without the supervision and physical presence of the human operator. The system is illustrated under a Search and Rescue (SAR) scenario to detect and locate victims inside a simulated office building. The navigation problem is modelled as a Partially Observable Markov Decision Process (POMDP) and solved in real time through the Augmented Belief Trees (ABT) algorithm. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. Experimental results show the robustness of the proposed framework to detect victims at various levels of location uncertainty. The proposed system ensures personal safety by letting the UAV to explore dangerous environments without the intervention of the human operator. View Full-Text
Keywords: partially observable Markov decision process (POMDP); machine learning; search and rescue (SAR); probabilistic decision-making; embedded systems; computer vision; autonomous system; unmanned aerial system (UAS); path planning; artificial intelligence partially observable Markov decision process (POMDP); machine learning; search and rescue (SAR); probabilistic decision-making; embedded systems; computer vision; autonomous system; unmanned aerial system (UAS); path planning; artificial intelligence
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MDPI and ACS Style

Sandino, J.; Vanegas, F.; Maire, F.; Caccetta, P.; Sanderson, C.; Gonzalez, F. UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments. Remote Sens. 2020, 12, 3386. https://doi.org/10.3390/rs12203386

AMA Style

Sandino J, Vanegas F, Maire F, Caccetta P, Sanderson C, Gonzalez F. UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments. Remote Sensing. 2020; 12(20):3386. https://doi.org/10.3390/rs12203386

Chicago/Turabian Style

Sandino, Juan, Fernando Vanegas, Frederic Maire, Peter Caccetta, Conrad Sanderson, and Felipe Gonzalez. 2020. "UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments" Remote Sensing 12, no. 20: 3386. https://doi.org/10.3390/rs12203386

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