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2024 | Book

New Developments and Environmental Applications of Drones

Proceedings of FinDrones 2023

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About this book

This volume presents the conference proceedings from FinDrones 2023. The book highlights recent drone technology developments by experts and academicians for applications in agriculture, forestry, and other industries. This iteration of FinDrones presents research using autonomous drones in various fields from environmental monitoring to farm robotics and from photogrammetry to search and rescue missions. Emphasis is placed on contextualizing the conference presentations and content to Finland and the unique challenges typical to the region. The work will interest academicians, entrepreneurs, and professionals involved in remote sensing applications of unmanned aerial vehicles and enthusiasts of drone technological developments.

Table of Contents

Frontmatter
Wild Swarms: Autonomous Drones for Environmental Monitoring and Protection
Abstract
In this paper, we present an example case study of how an autonomous swarm-based Multi-Robot System (MRS) could contribute to environmental protection. Our proof-of-concept simulation involves using a collective intelligence framework to manage a drone swarm tasked with protecting a wilderness area over a long period of time. In the proposed ambitious and futuristic Concept of Operation (ConOps), a self-sufficient and self-organizing colony of “drone wardens” is left to autonomously plan and execute thousands of individual reconnaissance missions, attempting to maintain the best possible situational awareness while simultaneously managing its resources and responding to relevant actors and events in the environment. Specifically, we have devised a scenario in which two types of external actors (hikers (blue team) and bears (red team)) should elicit a different collective response from the swarm: whilst the former should only be checked on from time to time to ensure their safety, the latter should be monitored continuously during their crossing of the park. Numerical experiments, in which a variety of simple but robust decision-making algorithms were tested, demonstrate our system’s ability to support the relevant swarming behaviour, such as efficient division of labour and recruitment, in the pursuit of this mission.
Fabrice Saffre, Hannu Karvonen, Hanno Hildmann
Connecting Different Drone Operations with the Farm Robotic Management
Abstract
Drones can be a crucial part in future farm automation and robotization. The drone technologies can offer relatively cheap, inclusive, and advanced smart solutions without being contact with the crops or soil. With drones, farmers can produce accurate measurements on demand of the interested phenomenon and can perform small-scale operations such as pesticide spraying or fertilizer spreading. While the automation and robotics in arable farming have continuously advanced, it is essential to also consider the role of different drone technologies that are so far developed separately from agricultural ground robotics. We developed eight versatile drone operation use cases to study the integration capabilities of the drone technologies with the future mission management of heterogeneous robotics. As results, we give an overview of the drone applications and present our data integration methodologies. With the mission control center approach, we developed four methodologies for the connectivity with the standard of Internet of Things messaging: connecting the autopilot of the drone directly enabling real-time data flow, connecting to the ground station, connecting the payload, or connecting by offline to the data processing service. As the drone technologies are developing rapidly, new solutions enabling third-party applications are supporting connectivity in the heterogeneous multirobot systems. However, with the drone operations, the safety issues can be more critical when compared with the ground robotics working in the same tasks.
Jere Kaivosoja, Kari Kolehmainen, Oskar Marko, Ari Ronkainen, Nina Pajević, Marko Panić, Sergio Vélez, Mar Ariza-Sentis, João Valente, Juha-Pekka Soininen
Is Alice Really in Wonderland? UWB-Based Proof of Location for UAVs with Hyperledger Fabric Blockchain
Abstract
Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations.
Lei Fu, Paola Torrico Morón, Jorge Peña Queralta, David Hästbacka, Harry Edelman, Tomi Westerlund
Accuracy Assessment of UAS Photogrammetry with GCP and PPK-Assisted Georeferencing
Abstract
Establishing a dense, well-distributed ground control point (GCP) network for unmanned aerial system (UAS) surveys can be time-consuming and impractical. Recent availability of UASs capable of GNSS-assisted aerial triangulation (AAT) has provided an alternative method, wherein the refinement of the positional accuracy of camera stations via, for example, post-processing kinematic (PPK) correction reduces the need for GCPs. Studies have highlighted how AAT can provide nearly equal accuracy to GCP-based georeferencing, especially if at least one GCP is utilized for bias correction. However, results on the utility of more than one GCP together with AAT are scarce or mixed. This study explores how the number of GCPs affects model accuracy when mapping a ~1 km2 site with a UAS capable of PPK correction. Also, a comparison between two different local base stations and a virtual reference station (VRS) is provided. Based on analysis with 3D checkpoints, increasing the number of GCPs provided only negligible improvements in horizontal accuracy. However, significant improvement is seen in vertical accuracy when increasing the number of GCPs, with the VRS providing the most accurate results. The results indicate that UAS surveys with AAT may benefit from utilization of multiple GCPs.
Anssi Rauhala
Simulation Analysis of Exploration Strategies and UAV Planning for Search and Rescue
Abstract
Aerial scans with unmanned aerial vehicles (UAVs) are becoming more widely adopted across industries, from smart farming to urban mapping. An application area that can leverage the strength of such systems is search and rescue (SAR) operations. However, with a vast variability in strategies and topology of application scenarios, as well as the difficulties in setting up real-world UAV-aided SAR operations for testing, designing an optimal flight pattern to search for and detect all victims can be a challenging problem. Specifically, the deployed UAV should be able to scan the area in the shortest amount of time while maintaining high victim detection recall rates. Therefore, low probability of false negatives (i.e., high recall) is more important than precision in this case. To address the issues mentioned above, we have developed a simulation environment that emulates different SAR scenarios and allows experimentation with flight missions to provide insight into their efficiency. The solution was developed with the open-source ROS framework and Gazebo simulator, with PX4 as the autopilot system for flight control, and YOLO as the object detector.
Phuoc Nguyen Thuan, Jorge Peña Queralta, Tomi Westerlund
Evaluating the Performance of Multi-scan Integration for UAV LiDAR-Based Tracking
Abstract
Drones have become essential tools in a wide range of industries, including agriculture, surveying, and transportation. However, tracking unmanned aerial vehicles (UAVs) in challenging environments, such as cluttered or GNSS-denied environments, remains a critical issue. Additionally, UAVs are being deployed as part of multi-robot systems, where tracking their position can be essential for relative state estimation. In this chapter, we evaluate the performance of a multi-scan integration method for tracking UAVs in GNSS-denied environments using a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm’s ability to track a UAV in a large open area at various distances and speeds. Our quantitative analysis shows that while “tracking by detection” using a constant-velocity model is the only method that consistently tracks the target, integrating multiple scan frequencies using a KF achieves lower position errors and represents a viable option for tracking UAVs in similar scenarios.
Iacopo Catalano, Jorge Peña Queralta, Tomi Westerlund
Applications and Challenges Related to the Use of Unmanned Aircraft Systems in Environment Monitoring
Abstract
This chapter gives an overview of the latest research and development activities conducted by VTT regarding environmental monitoring using unmanned aircraft systems (UAS) and discusses the associated challenges. An AI-based drone swarm technology in a unified framework can provide situational awareness and decision support tools for wildfire monitoring. The monitoring of floating waste from an unmanned aircraft (UA) with optical sensors suggests that multi-imaging with near-infrared (NIR) hyperspectral (HS), thermal infrared (TIR), and multicolor (RGB) sensors is a promising method for separating floating plastic waste from organic material. Monitoring of tailing ponds of mines with onboard hyperspectral and multispectral sensors indicated hints of seepage or water in spectral signatures of vegetation and ground along with general structural information, particularly of tailing pond dams. Hyperspectral data acquired by a UAS is well suited for monitoring vegetation’s biochemical composition, moisture content, and biodiversity since it offers unprecedented spatial resolution with pixel sizes comparable to the basic vegetation elements, leaves or flowers. VTT demonstrated the applicability of novel vegetation analysis algorithms based on the theory of spectral invariant theory to such ultra-high-resolution HS imagery for vegetation trait retrieval. The challenges related to the use of UAS are multifaceted. These include connectivity technologies and protocols, the operational limitations of UA, and the application of artificial intelligence (AI), data fusion, and machine learning methods. Also, the legislative demand for autonomous UAS operations, significantly beyond visual line of sight (BVLOS), requires a range of U-space services.
Jukka Sassi, Vadim Kramar, Matti Mõttus, Olli Ihalainen, Sami Siikanen
UAV-Borne Measurements of Solar-Induced Chlorophyll Fluorescence (SIF) at a Boreal Site
Marika Honkanen, Pauli Heikkinen, Alasdair MacArthur, Tea Thum, Rigel Kivi, Hannakaisa Lindqvist
Thermal Drone Images as a Predictor of Soil Moisture Values
Abstract
Drones are one of the latest additions to the arsenal of technologies for precision farming. Their use is still restricted but is expected to grow as farmers get used to them, and they get positive peer reviews of their usefulness and more practical use cases for them. To provide a practical use case, we have studied the use of thermal drone images as a predictor of soil moisture values. We used a SenseFly eBee X fixed wing drone to monitor a barley field in Saarijärvi, Central Finland, during the growing season of 2022. Images were processed with Pix4D software and analyzed using Python programming language. In addition to thermal images, we used Soil Scout soil moisture sensors to acquire actual soil moisture values. The images were compared with the soil sensor data to see how well and under what conditions they predicted actual soil moisture values. The results showed that without any preconditions plain thermal images do not predict soil moisture values well, but with certain conditions mild predictability can be achieved.
Janne Kalmari, Iita Appelgren, Gilbert Ludwig, Hannu Haapala
Backmatter
Metadata
Title
New Developments and Environmental Applications of Drones
Editors
Tomi Westerlund
Jorge Peña Queralta
Copyright Year
2024
Electronic ISBN
978-3-031-44607-8
Print ISBN
978-3-031-44606-1
DOI
https://doi.org/10.1007/978-3-031-44607-8

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