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UAV-Based Sensing and Autonomous Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 1396

Special Issue Editor


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Guest Editor
School of Engineering and Computing, University of Central Lancashire, Preston PR1 2HE, UK
Interests: mobile robots; multi-agent systems; Internet; artificial intelligence; remotely operated vehicles; road safety; road vehicles; traffic engineering computing; Internet of Things; automobiles; autonomous aerial vehicles
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Special Issue Information

Dear Colleagues,

Unmanned aerial systems (UASs) present the following operational advantages over conventional vehicles: (i) they need no physical road infrastructure thanks to a direct, constant, and high travel speed; (ii) less exposure to traffic congestion; (iii) ability to operate in dangerous and disaster-ridden environments; (iv) ability to be deployed easily and rapidly; (v) ability to cover a large terrain; and (vi) adjustable height to better meet and serve various requirements. Unmanned aerial vehicles (UAVs) are becoming increasingly automated to fulfill numerous tasks alone or within swarms (e.g., search and rescue (SAR), medical supply delivery, humanitarian aid, environmental monitoring, photography/filming, inspection/maintenance, mapping, and law enforcement), as self-awareness and control systems, benefitting from advancing sensor technologies, actuators, and artificial intelligence (AI) techniques, improve. Sensors are the main self-awareness components (i) in perceiving the environment through continuous vehicle environmental interaction via sensor fusion and (ii) establishing various missions beyond the visual line of sight (BVLOS) through effective decision making depending on the assigned task requirements.

In this Special Issue, we are keen to showcase the most recent sensor technologies that have been or are being developed for UAVs. More explicitly, we would like to analyze how sensors and sensor fusion are contributing to UAV automation, i.e., the role of sensors and sensor fusion in increasing the efficacy of UAV autonomous decision making. Therefore, we would like to invite the academic and industrial research community to contribute original research and review articles to this Special Issue.

Dr. Kaya Kuru
Guest Editor

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Keywords

  • unmanned aerial vehicles (UAVs)
  • unmanned aerial systems (UASs)
  • autonomous vehicles
  • sensor fusion
  • autonomous decision making
  • vehicle automation

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Published Papers (1 paper)

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Research

36 pages, 20759 KB  
Article
Autonomous UAV Landing and Collision Avoidance System for Unknown Terrain Utilizing Depth Camera with Actively Actuated Gimbal
by Piotr Łuczak and Grzegorz Granosik
Sensors 2025, 25(19), 6165; https://doi.org/10.3390/s25196165 - 5 Oct 2025
Viewed by 885
Abstract
Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color [...] Read more.
Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color data when lidar is used, limited obstacle perception when only color imaging is used, a low field of view from a single RGB-D sensor, or the requirement for the landing spot to be prepared in advance. In this paper, a new approach is proposed where an RGB-D camera mounted on a gimbal is used. The gimbal is actively actuated to counteract the limited field of view while color images and depth information are provided by the RGB-D camera. Furthermore, a combined UAV-and-gimbal-motion strategy is proposed to counteract the low maximum range of depth perception to provide static obstacle detection and avoidance, while preserving safe operating conditions for low-altitude flight, near potential obstacles. The system is developed using a PX4 flight stack, CubeOrange flight controller, and Jetson nano onboard computer. The system was flight-tested in simulation conditions and statically tested on a real vehicle. Results show the correctness of the system architecture and possibility of deployment in real conditions. Full article
(This article belongs to the Special Issue UAV-Based Sensing and Autonomous Technologies)
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