Measurements from the University of Colorado RAAVEN Uncrewed Aircraft System during ATOMIC

Between 24 January and 15 February 2020, small uncrewed aircraft systems (sUASs) were deployed to Morgan Lewis (Barbados) as part of the Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC), a sister project to the ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte (EUREC4A) project. The observations from ATOMIC and EUREC4A were aimed at improving our understanding of trade-wind cumulus clouds and the environmental regimes supporting them and involved the deployment of a wide variety of observational assets, including aircraft, ships, surface-based systems, and profilers. The current paper describes ATOMIC observations obtained using the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) sUAS. This platform collected nearly 80 h of data throughout the lowest kilometer of the atmosphere, sampling the near-shore environment upwind from Barbados. Data from these platforms are publicly available through the National Oceanic and Atmospheric Administration's National Center for Environmental Intelligence (NCEI) archive. The primary DOI for the quality-controlled dataset described in this paper is https://doi.org/10.25921/jhnd-8e58 (de Boer et al., 2021).

ALGORITHM FOR PRECISE LANDING OF MULTIROTOR UNMANNED AIRCRAFT SYSTEMS AT AN AUTONOMOUS CHARGING STATION

In this article, we propose an algorithm for accurately landing multirotor (quadcopters, hexacopters, etc.) unmanned aerial vehicles (UAVs) at an autonomous charging station. This article also presents methods for locating the charging station and landing the UAV at night. Section 1 describes the general sequential landing procedures. Section 2 describes methods for detecting the ArUco marker and evaluating its position and orientation using the OpenCV computer vision library and shows the recognition result. In section 3, the precise landing algorithm is analyzed in detail, and a block diagram of the algorithm is given. Section 4 discusses the integration of the night vision camera into the landing algorithm.

METHODOLOGICAL FOUNDATIONS OF COMPETITION ON THE SKILL OF USAGE OF UNMANNED AIRCRAFT SYSTEMS IN EMERCOM OF RUSSIA

В статье рассмотрены вопросы, касающиеся организации проведения соревнований по мастерству применения беспилотных авиационных систем в МЧС России. Актуальность материала статьи обусловлена, с одной стороны, распространением в большинстве организаций конкурсов профессионального мастерства, помогающих повысить качество подготовки и креативного мышления специалистов, а с другой - недостаточным опытом их проведения в МЧС России. По результатам анализа материалов публикаций о рассматриваемой деятельности в учебных заведениях и трудовых коллективах страны авторами выработаны и представлены в статье рекомендации по организации интересуемых соревнований. The article discusses the issues related to performance of competitions on the skill of usage of unmanned aircraft systems in EMERCOM of Russia. The relevance of the article is, on the one hand, due to the spread of professional skill contests in most organizations that help to improve the quality of training and creative thinking of specialists, and on the other hand, due to insufficient experience of its conduction in EMERCOM of Russia. The authors have developed and presented the recommendations on the organization of interested competitions based on the results of analysis of the publication materials concerning discussed activities in educational institutions and labor collectives of our country.

SORA Methodology for Multi-UAS Airframe Inspections in an Airport

Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations requires demonstrating compliance with applicable regulations and a comprehensive understanding of the residual risk associated with the UAS operation. To support these activities and enable the safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has established a UAS regulatory framework that mandates the execution of safety risk assessment for UAS operations in order to gain authorization to carry out certain types of operations. Driven by this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment for UAS operations. However, existing work on SORA and its applications focuses mainly on single UAS operations, offering limited support for assuring operations conducted with multiple UAS and with autonomous features. Therefore, the work presented in this paper analyzes the application of SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS with autonomous features inside an airport. We present the decision-making process of each SORA step and its application to a multiple UAS scenario. The results shows that the procedures and safety features included in the Multi-AFI operation such as workspace segmentation, the independent multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can be conducted safely and can receive a positive evaluation from the competent authorities. We also present our key findings from the application of SORA and discuss how it can be extended to better support multi-UAS operations.

AERIAL SEEDING APPLICATION FOR REFORESTATION

Due to the presence in the forest fund of the Russian Federation of a sufficient number of areas that are inaccessible, unsuitable or inefficient for the use of ground-based mechanization tools (felling, burning, forest canopy, ravine-beam network, slopes), it is advisable to sow forest seeds from the air using unmanned aircraft systems.

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.