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Reconfigurable Sensor Drones

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

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 26589

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Special Issue Editors

Dr. Victoria Hodge

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Guest Editor

Digital Creativity Labs, Department of Computer Science, University of York, York, UK
Interests: drones; sensor and data fusion; pattern recognition; anomaly detection; artificial intelligence; data analytics

Prof. Dr. Jon Timmis

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Co-Guest Editor

Pro-Vice-Chancellor for Partnerships and Knowledge Exchange & Professor of Intelligent and Adaptive Systems, Department of Electronic Engineering, University of York, York, UK
Interests: drones; swarm robotics; anomaly detection; fault tolerance; fault recovery; modelling & simulation; artificial immune systems

Special Issue Information

Dear Colleagues,

The rapid development and growth of drones as remote sensing platforms, coupled with recent advances in the size and processing capabilities of instrumentation and data systems, have led to an increasing uptake of this technology. Cutting-edge developments include reconfigurable drones, autonomous drones and drone swarms.

As a result of these developments, the use of drones across the globe is increasing. They can be used for inspection, surveillance, monitoring, and data collection on infrastructure, buildings and environments across diverse domains as well as search and rescue. This motivates the need for flexible, autonomous and powerful decision making using drones that can be customised for different applications. Until very recently, drones have been task-specific and not reconfigurable. A reconfiguration ability using either software or hardware reconfiguration allows a drone or swarm of drones to disassemble and reassemble to form new configurations to adapt to different situations. Such modular systems can potentially lower overall costs by making more complex machines out of relatively few types of modules.

The Special Issue provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of reconfigurable sensor drones (including unmanned aerial vehicles (UAVs), unmanned water/underwater drones and unmanned ground vehicles (UGVs)); autonomous drones for multiple sensing domains; applications of reconfigurable drones for remote monitoring and inspection; swarm technology for drones and autonomous drone monitoring; associated developments in sensor technology, data processing, artificial intelligence, fault tolerance and communications; platforms for reconfigurable sensor drones (including swarms); and reconfigurable sensor drone design and sensing capabilities.

Prospective authors are invited to contribute to this Special Issue of Sensors (Impact Factor: 2.677 (2016); 5-Year Impact Factor: 2.964 (2016)) by submitting an original manuscript.

The scope of this Special Issue includes, but it is not limited to:

Reconfigurable sensor drones (including UAVs, UGVs and unmanned water/underwater drones),
Drones for multiple sensing applications,
Platforms for drones and drone swarms for multiple sensing applications,
Artificial intelligence and other platforms for controlling reconfigurable sensor drones,
Swarm technology for drone sensing applications including hardware and controller software,
Collaborative strategies and mechanisms to control multiple sensor drones,
Data storage on-board the drone and the transmission, retrieval and processing of these data for multiple applications,
Descriptions of multi-purpose algorithms applied to drone sensor data,
Artificial intelligence and data mining for reconfigurable sensor drone data fusion and analysis,
Anomaly detection, fault tolerance and fault recovery in sensor drones,
Security and cybersecurity issues and solutions for reconfigurable sensor drones,
Drone sensor design for multiple applications,
Improvements in drone sensor technology, particularly multi-application sensor technology,
Reconfigurable drone applications across diverse domains including but not limited to one or more of the following: agriculture, pest detection, livestock monitoring, forestry, land use monitoring, bio-security, atmospheric monitoring, biological/chemo-sensing tasks, natural disaster monitoring, environmental monitoring, pollution monitoring, flood monitoring, reef monitoring, volcanic monitoring, volcanic gas sampling, landfill monitoring, applications in the construction industry, infrastructure monitoring, thermal energy efficiency inspections, transport monitoring, telecommunications network monitoring, inspection of pipelines and transmission networks, inspection of wind farms and power stations, on- and offshore inspection of oil and gas platforms, mining, search and rescue, rapid response for emergencies, and, target tracking.

Dr. Victoria Hodge
Prof. Jon Timmis
Guest Editors

Manuscript Submission Information

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Keywords

sensor drones
reconfigurable drones
drone swarms
AI
anomaly detection
fault tolerance
fault recovery
inspection
monitoring
drone applications

Published Papers (5 papers)

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Research

43 pages, 28190 KiB

Open AccessArticle

Multipotent Systems: Combining Planning, Self-Organization, and Reconfiguration in Modular Robot Ensembles

by Oliver Kosak, Constantin Wanninger, Alwin Hoffmann, Hella Ponsar and Wolfgang Reif

Sensors 2019, 19(1), 17; https://doi.org/10.3390/s19010017 - 20 Dec 2018

Cited by 15 | Viewed by 4484

Abstract

Mobile multirobot systems play an increasing role in many disciplines. Their capabilities can be used, e.g., to transport workpieces in industrial applications or to support operational forces in search and rescue scenarios, among many others. Depending on the respective application, the hardware design and accompanying software of mobile robots are of various forms, especially for integrating different sensors and actuators. Concerning this design, robots of one system compared to each other can be classified to exclusively be either homogeneous or heterogeneous, both resulting in different system properties. While homogeneously configured systems are known to be robust against failures through redundancy but are highly specialized for specific use cases, heterogeneously designed systems can be used for a broad range of applications but suffer from their specialization, i.e., they can only hardly compensate for the failure of one specialist. Up to now, there has been no known approach aiming to unify the benefits of both these types of system. In this paper, we present our approach to filling this gap by introducing a reference architecture for mobile robots that defines the interplay of all necessary technologies for achieving this goal. We introduce the class of robot systems implementing this architecture as multipotent systems that bring together the benefits of both system classes, enabling homogeneously designed robots to become heterogeneous specialists at runtime. When many of these robots work together, we call the structure of this cooperation an ensemble. To achieve multipotent ensembles, we also integrate reconfigurable and self-descriptive hardware (i.e., sensors and actuators) in this architecture, which can be freely combined to change the capabilities of robots at runtime. Because typically a high degree of autonomy in such systems is a prerequisite for their practical usage, we also present the integration of necessary mechanisms and algorithms for achieving the systems’ multipotency. We already achieved the first results with robots implementing our approach of multipotent systems in real-world experiments as well as in a simulation environment, which we present in this paper. Full article

(This article belongs to the Special Issue Reconfigurable Sensor Drones)

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14 pages, 1868 KiB

Open AccessArticle

Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling

by Qijun Gu, Drew R. Michanowicz and Chunrong Jia

Sensors 2018, 18(12), 4363; https://doi.org/10.3390/s18124363 - 10 Dec 2018

Cited by 71 | Viewed by 7735

Abstract

The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs. Full article

(This article belongs to the Special Issue Reconfigurable Sensor Drones)

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28 pages, 586 KiB

Open AccessArticle

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

by Chabha Hireche, Catherine Dezan, Stéphane Mocanu, Dominique Heller and Jean-Philippe Diguet

Sensors 2018, 18(12), 4266; https://doi.org/10.3390/s18124266 - 4 Dec 2018

Cited by 13 | Viewed by 3688

Abstract

This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context. Full article

(This article belongs to the Special Issue Reconfigurable Sensor Drones)

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27 pages, 5924 KiB

Open AccessFeature PaperArticle

Multi-UAV Path Planning for Autonomous Missions in Mixed GNSS Coverage Scenarios

by Flavia Causa, Giancarmine Fasano and Michele Grassi

Sensors 2018, 18(12), 4188; https://doi.org/10.3390/s18124188 - 29 Nov 2018

Cited by 33 | Viewed by 5777

Abstract

This paper presents an algorithm for multi-UAV path planning in scenarios with heterogeneous Global Navigation Satellite Systems (GNSS) coverage. In these environments, cooperative strategies can be effectively exploited when flying in GNSS-challenging conditions, e.g., natural/urban canyons, while the different UAVs can fly as independent systems in the absence of navigation issues (i.e., open sky conditions). These different flight environments are taken into account at path planning level, obtaining a distributed multi-UAV system that autonomously reconfigures itself based on mission needs. Path planning, formulated as a vehicle routing problem, aims at defining smooth and flyable polynomial trajectories, whose time of flight is estimated to guarantee coexistence of different UAVs at the same challenging area. The algorithm is tested in a simulation environment directly derived from a real-world 3D scenario, for variable number of UAVs and waypoints. Its solution and computational cost are compared with optimal planning methods. Results show that the computational burden is almost unaffected by the number of UAVs, and it is compatible with near real time implementation even for a relatively large number of waypoints. The provided solution takes full advantage from the available flight resources, reducing mission time for a given set of waypoints and for increasing UAV number. Full article

(This article belongs to the Special Issue Reconfigurable Sensor Drones)

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27 pages, 4167 KiB

Open AccessArticle

User-Configurable Timing and Navigation for UAVs

by Sigurd M. Albrektsen and Tor Arne Johansen

Sensors 2018, 18(8), 2468; https://doi.org/10.3390/s18082468 - 30 Jul 2018

Cited by 21 | Viewed by 4009

Abstract

As the use of unmanned aerial vehicles (UAVs) for industrial use increases, so are the demands for highly accurate navigation solutions, and with the high dynamics that UAVs offer, the accuracy of a measurement does not only depend on the value of the measurement, but also the accuracy of the associated timestamp. Sensor timing using dedicated hardware is the de-facto method to achieve optimal sensor performance, but the solutions available today have limited flexibility and requires much effort when changing sensors. This article presents requirements and suggestions for a highly accurate, reconfigurable sensor timing system that simplifies integration of sensor systems and navigation systems for UAVs. Both typical avionics sensors, like GNSS receivers and IMUs, and more complex sensors, such as cameras, are supported. To verify the design, an implementation named the SenTiBoard was created, along with a software support package and a baseline sensor-suite. With the solution presented in this paper we get a measurement resolution of 10 nanoseconds and we can transfer up to 7.6 megabytes per second. If the sensor suite includes a GNSS receiver with a pulse-per-second (PPS) reference, the sensor measurements can be related to an absolute time reference (UTC) with a clock drift of 1.9 microseconds per second RMS. An experiment was carried out, using a Mini Cruiser fixed-wing UAV, where errors in georeferencing infrared images were reduced with a factor of 4 when compared to a software synchronization method. Full article

(This article belongs to the Special Issue Reconfigurable Sensor Drones)

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