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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 24031

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Special Issue Editors

Prof. Dr. Boaz Ben-Moshe

E-Mail Website
Guest Editor

Department of Computer Science, Ariel University, Ariel 4070000, Israel
Interests: indoor navigation; mapping and SLAM; autonomous micro robotics; bio-inspired robotics; visibility graphs
Special Issues, Collections and Topics in MDPI journals

Dr. Nir Shvalb

E-Mail Website
Guest Editor

Department of Mechanical Engineering and Mechatronics, Faculty of Engineering, Ariel University, P.O. Box 3, Ariel 407000, Israel
Interests: theoretical robotics; global motion planning; medical robotics; swarm robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, Unmanned Aerial Vehicles (UAVs) have been used in a variety of applications. Such drones are commonly equipped with a wide range of sensors, including GNSS, IMU, Gimbaled camera, optical flow, Lidar, and ultrasonic and stereo depth sensors. Yet, the vision of an autonomous swarm of drones that can perform applications such as door-to-door delivery in a danced urban region is still far from being a reality. This Special Issue will focus on general-purpose autonomous micro drones that can perform complicated missions such as robust and continuance SLAM (Simultaneous Localization And Mapping), real-time 3D motion planning, swarm cooperation, and edge-based machine learning for sensor fusion. We welcome original, state-of-the-art studies in the areas that contribute to academia and industry. The Special Issue will cover but is not limited to the following:

Adaptive sensor fusion methods for Autonomous UAVs and micro drones;
In-air self-calibration and sensor failure diagnosis;
Deep learning-based sensor fusion and state detection;
Bio-inspired challenges: return to home (RTH), visual navigation, intelligent landing, sense and avoid;
Indoor navigation and mapping;
SWARM cooperation and transfer learning between drones;
Case studies: on intelligent autonomous micro drones.

Prof. Dr. Boaz Ben-Moshe
Prof. Dr. Nir Shvalb
Guest Editors

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Keywords

autonomous sensors for micro drones
bioinspired UAVs
robust SLAM methods
edge-based deep Learning for sensor fusion

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Published Papers (4 papers)

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Research

20 pages, 2933 KB

Open AccessArticle

The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds

by Aleksandr Lapušinskij, Ivan Suzdalev, Nikolaj Goranin, Justinas Janulevičius, Simona Ramanauskaitė and Gintautas Stankūnavičius

Sensors 2021, 21(17), 5821; https://doi.org/10.3390/s21175821 - 29 Aug 2021

Cited by 19 | Viewed by 5398

Abstract

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method. Full article

(This article belongs to the Special Issue Sensor for Autonomous Drones)

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14 pages, 13353 KB

Open AccessArticle

Vision-Less Sensing for Autonomous Micro-Drones

by Simon Pikalov, Elisha Azaria, Shaya Sonnenberg, Boaz Ben-Moshe and Amos Azaria

Sensors 2021, 21(16), 5293; https://doi.org/10.3390/s21165293 - 5 Aug 2021

Cited by 7 | Viewed by 9109

Abstract

This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness. Full article

(This article belongs to the Special Issue Sensor for Autonomous Drones)

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18 pages, 752 KB

Open AccessArticle

Improved GNSS Localization and Byzantine Detection in UAV Swarms

by Shlomi Hacohen, Oded Medina, Tal Grinshpoun and Nir Shvalb

Sensors 2020, 20(24), 7239; https://doi.org/10.3390/s20247239 - 17 Dec 2020

Cited by 12 | Viewed by 3000

Abstract

Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process. Full article

(This article belongs to the Special Issue Sensor for Autonomous Drones)

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20 pages, 1527 KB

Open AccessArticle

Autonomous Toy Drone via Coresets for Pose Estimation

by Soliman Nasser, Ibrahim Jubran and Dan Feldman

Sensors 2020, 20(11), 3042; https://doi.org/10.3390/s20113042 - 27 May 2020

Cited by 4 | Viewed by 3209

Abstract

A coreset of a dataset is a small weighted set, such that querying the coreset provably yields a (

1 + ε

)-factor approximation to the original (full) dataset, for a given family of queries. This paper suggests accurate coresets ( [...] Read more.

A coreset of a dataset is a small weighted set, such that querying the coreset provably yields a (

1 + ε

)-factor approximation to the original (full) dataset, for a given family of queries. This paper suggests accurate coresets (

ε = 0

) that are subsets of the input for fundamental optimization problems. These coresets enabled us to implement a “Guardian Angel” system that computes pose-estimation in a rate

> 20

frames per second. It tracks a toy quadcopter which guides guests in a supermarket, hospital, mall, airport, and so on. We prove that any set of n matrices in

R^{d \times d}

whose sum is a matrix S of rank r, has a coreset whose sum has the same left and right singular vectors as S, and consists of

O (d r) = O (d^{2})

matrices, independent of n. This implies the first (exact, weighted subset) coreset of

O (d^{2})

points to problems such as linear regression, PCA/SVD, and Wahba’s problem, with corresponding streaming, dynamic, and distributed versions. Our main tool is a novel usage of the Caratheodory Theorem for coresets, an algorithm that computes its set in time that is linear in its cardinality. Extensive experimental results on both synthetic and real data, companion video of our system, and open code are provided. Full article

(This article belongs to the Special Issue Sensor for Autonomous Drones)

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