Drone Fleet Deployment Strategy for Large Scale Agriculture and Forestry Surveying

International audienceAgriculture drones offer clear advantages over other monitoring methods including satellite imaging, manned scouting, and manned aircraft. However, for large scale areas, such as large forestry and agriculture mapping problems, the single drone is hard to accomplish its mission of mapping in a relatively short time period of 30 to 45 minutes. In addition, in large forestry mapping, camera, communication, and payload settings may further reduce the maximum endurance of drones in the air. With a single drone, the total required mission time to cover all the area is prolonged, not only producing a high cost for a drone service provider but also having more uncertainty. While with multiple drones, or a fleet of drones, it is possible to identify a globally optimized solution to reduce the total required mission time. In this paper, we mainly discuss the strategy of drone fleet deployment for large scale area surveying. Three key parts are analyzed, including a fleet of drones, cooperative coverage path planning, communication and data processing. The associated state-of-the-art solutions are listed and reviewed. In addition, in this paper, the key operational constraints for large scale agriculture and forestry surveying are analyzed. It should be pointed out that, from a comprehensive point of view, a drone fleet deployment for large scale surveying could attract more attention from the commercial drone industry

Real Time Implementation of Amphibious Unmanned Aerial Vehicle System for Horticulture

Automating the tasks that require manpower has been considered as an area of active research in science and technology. Challenges in designing such systems include accuracy in the parameters of performance, minimal hardware, cost-efficiency, and security. The efficiency of drones designed for replacing humans is often evaluated using their weight, flying time, and power consumption. Herein, the prototype-based Drone model has been designed and discussed for horticulture applications. In this model, a horticulture drone has been designed for structuring and cutting of plants in street interstates. This methodology focuses on automation engineering that is utilized for cutting the plants in less time and less power, thereby diminishing the contamination that may happen by utilizing fuels. The epic part of this plan includes the less weight drone predesigned using Computer-Aided Three-Dimensional Interactive Application (CATIA) V5 Software. The throttle for the motors is adjusted at 50% to get the required thrust for the Unmanned Aerial Vehicle (UAV) to fly. Experimental results show that the horticulture drone has comparatively more flying time and less power consumption.Keywords— CATIA; UAV; Automation; Thrust; Throttle

Embracing drones and the Internet of drones systems in manufacturing – An exploration of obstacles

The manufacturing sector attributes the growing prominence of Drones and the Internet of Drones (IoD) systems to their multifaceted utility in delivery, process monitoring, infrastructure inspection, inventory management, predictive maintenance, and safety inspections. Despite their potential benefits, adopting these technologies faces significant obstacles that need systematic identification and resolution. The current literature inadequately addresses the barriers impeding the adoption of Drones and IoD systems in manufacturing, indicating a research gap. This study bridges this gap by providing comprehensive insights and facilitating the organisational transition towards embracing Drone and IoD technologies. This research identifies 20 critical barriers to deploying Drones and IoD in manufacturing. These barriers are validated through a global quantitative survey of 120 Drone experts and analysed via Exploratory Factor Analysis (EFA). EFA categorises these challenges into six distinct dimensions. Utilising the Analytical Hierarchy Process (AHP), these dimensions and individual barriers are ranked, incorporating feedback from five Drone specialists. The study highlights ‘Safety and Human Resource Barriers’ and ‘Payload Capacity and Battery Barriers’ as the most predominant obstacles. Key concerns include limited battery life, explosion risks, and potential damage to assets and individuals. This research significantly advances the existing literature by presenting a practical methodology for categorising and prioritising Drone and IoD adoption barriers. Employing EFA and AHP offers a globally relevant framework for stakeholders to strategically address these challenges, advancing the integration of drones and IoD systems in the manufacturing domain

A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs

This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of Unmanned Aerial Vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map

FROM REALITY-BASED MODEL TO GIS PLATFORM. MULTI-SCALAR MODELING FOR IRRIGATED LANDSCAPE MANAGEMENT IN THE PAVIA PLAIN

This research aims to define a low-cost replicable methodology for obtaining fast multiscale information models. The experiments carried out were conducted by researchers from Dada LAB and PLAY experimental Laboratories of the University of Pavia, Department of Civil Engineering and Architecture, on the case study of the irrigated landscape of the Pavia plain. The entire work process was developed according to a low-cost purpose, starting from fast acquisition activities with UAV instruments, to the processing of photogrammetric data, urban and detailed scale modelling with open-source software, to the census, filing, and computerisation of the model. The resulting product is configured as a multiscale reality-based information system. A census card is associated with each constituent element of the model (crops, canals, valuable hydraulic artefacts). Connection to the GIS platform allows the user to query the model. The result is a digital system oriented to facilitate the management of the agricultural and irrigation landscape, and to digitally document and preserve the heritage of historical hydraulic existing artefacts. Two different GIS platforms for structuring the information system were tested. The first involved a high-budget solution using ESRI ArcGIS Pro/ArcSCENE software, and the second involved using QGIS software, an Open-Source Geographic Information System, to develop an accessible information system without license fees, to evaluate the advantages and disadvantages of low-cost processes

Indirect structural health monitoring (iSHM) of transport infrastructure in the digital age

Workshop reportCopyright © Joint Research Centre (European Commission). The existing European motorway infrastructure network is prone to ageing and subject to natural events (e.g. climate change) and hazards (e.g. earthquakes), necessitating immediate actions for its maintenance and safety. Within this context, the structural health monitoring (SHM) framework allows a quantitative assessment of the structural integrity, serviceability and performance, facilitating better-informed decisions for the management of the existing infrastructure. The European Commission Joint Research Centre (JRC) established the exploratory research project MITICA (Monitoring Transport Infrastructures with Connected and Automated vehicles) to investigate the opportunity to use novel methods for infrastructure motoring, aiming at the efficient maintenance of the European aging road infrastructure. This report summarizes the discussion and the outcomes of a workshop held at the JRC in Ispra (Italy) on June 6-7 2022, as part of the MITICA project. Considering the EU priority “A Europe fit for the digital age”, the workshop was dedicated to SHM and its application to civil infrastructure, focusing on innovative indirect structural health monitoring (iSHM) approaches that rely on the vehicle-bridge interaction and the deployment of sensor-equipped vehicles for the monitoring of the existing bridge infrastructure. The report aims to become a reference document in the area of iSHM using passing vehicles, for both scholars and policy makers

“Make no little plans”: Impactful research to solve the next generation of transportation problems

The transportation science research community has contributed to numerous practical and intellectual innovations and improvements over the last decades. Technological advancements have broadened and amplified the potential impacts of our field. At the same time, the world and its communities are facing greater and more serious challenges than ever before. In this paper, we call upon the transportation science research community to work on a research agenda that addresses some of the most important of these challenges. This agenda is guided by the sustainable development goals outlined by the United Nations and organized into three areas: (1) well-being, (2) infrastructure, and, (3) natural environment. For each area, we identify current and future challenges as well as research directions to address those challenges

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen