Keywords: UAS Applications, Aerial Robotic Manipulation
Abstract: Unmanned aerial vehicles (UAVs) are being increasingly established for autonomous contacted-based inspection of industrial assets, reducing the risk of errors by human operators. However, challenges remain in the navigation under unreliable Global Navigation Satellite System (GNSS) signals, or in the detection and interaction with the inspection surface, which limits the autonomy level of current UAV industrial technologies. This paper presents a semi-autonomous framework which combines automated target detection and interaction in GNSS-denied environments, with supervision commands by a human operator, to increase both safety and reliability. Our framework is composed of: (i) a camera-based onboard odometry solution for positioning the UAV in GNSS-denied conditions, (ii) a target-detection and filtering algorithm which estimates the orientation and position of the interaction target and (iii) a visual servoing strategy for approaching and contacting the target. The proposed framework is developed completely in ROS, and can be used with any commercial UAV. The framework is validated through outdoor flights, where a UAV detects and contacts a target on a vertical pipe.

Keywords: Multirotor Design and Control, Reliability of UAS, Technology Challenges
Abstract: This paper analyses the problem of mechanical vibrations on Flight Controllers (FCs) of Multi-Rotor Aerial Vehicles (MRAVs) and proposes solutions based on vibration theory. First, we analyze the raw Inertial Measurement Unit (IMU) data obtained from real flights to understand the dynamical characteristics of a baseline damping configuration. We confirm that the motor-propeller units are the main source of vibration in these systems. We then develop two models used to understand how to effectively damp vibrations on IMU. We improve the baseline configuration using commonly available components placed according to a theoretical analysis and we discuss the experimental results. The new damping configuration greatly decreases the amplitude of vibrations on acceleration and angular velocity measurements.

Keywords: UAS Applications, Autonomy
Abstract: This work introduces LUCAS, an open-source framework designed to control and monitor highly autonomous UAV systems. This framework is composed of two modules, the Control Manager Finite-State-Machine (FSM), an efficient and easily extensible state machine that controls the behavior of the robot during the mission, and the Cascade Controller, which handles the commands to the low-level autopilot. The methodology followed to develop the framework has been the use of C++ and a modularized implementation, to separate communications from core functionality and allow the use of diverse middlewares, in this case, ROS and ROS2. The system can be integrated with other software nodes to form a complete autonomous setup, which has been successfully tested in a simulated environment and in a real scenario, where a quad-rotor has to perform an indoor inspection in a building under construction.

Keywords: UAS Applications
Abstract: The widespread use of aerial robots in inspection, search and rescue, and monitoring has created a growing need for intuitive human-drone interfaces. These aim to streamline and enhance the user interaction and collaboration process during drone navigation, ultimately expediting mission success and accommodating users’ inputs. In this paper, we present a novel human-drone mixed reality interface that aims to (a) increase human-drone spatial awareness by sharing relevant spatial information and representations between the human equipped with a Head Mounted Display (HMD) and the robot and (b) enable safer and intuitive human-drone interactive and collaborative navigation in unknown environments beyond the simple command and control or teleoperation paradigm. Our framework is validated through extensive user studies and experiments conducted in simulated post-disaster scenarios, with performance compared to traditional First-Person View (FPV) control systems. Multiple tests on several users underscore the advantages of the proposed solution, which offers intuitive and natural interaction with the system. This demonstrates the solution’s ability to assist humans during a drone navigation mission, ensuring its safe and effective execution.

Keywords: Navigation, Autonomy, UAS Applications
Abstract: In this paper, we present a method to control the UAV to follow the power line with a specific position and orientation based only on the measurement of the magnetic field generated by the current flow in the power line. In this way, it is possible to localize the UAV with respect to the power line without the need for additional sensors, even in poor visibility conditions. The measurements from four magnetometers attached to the UAV are used to solve an optimization problem that involves determining the relative pose of the UAV with respect to the power line. Based on the relative pose, the UAV is controlled to follow the power line in a predefined position and orientation. Experiments in a test setup have confirmed that the method is applicable in a realistic environment.

Keywords: Aerial Robotic Manipulation, UAS Applications, Energy Efficient UAS
Abstract: This paper presents the application of an aerial-deployable dual arm rolling robot developed for the realization of maintenance operations on power lines, validated through field tests in a real power line. The system consists of a quadrotor used as carrier platform for the transportation, deployment and retrieval of a lightweight and compliant anthropomorphic dual arm system (LiCAS). The arms are equipped with a drive wheel that allows them to move along the cable to conduct the installation of devices while the aerial platform stays at the landing area. The proposed approach avoids the problems of operating while flying in terms of positioning accuracy and energy efficiency, reducing also significantly the load on the power line compared to the case in which the multi-rotor has to perch. The paper describes the mechanisms implemented for the deployment and retrieval of the arms on the power line and for the installation of a customized model of bird flight diverter on the power line, as well as the system architecture, reporting results and practical aspects derived from the experimental validation.

Keywords: Payloads, UAS Applications
Abstract: Unmanned Aircraft Systems (UAS) for spraying plant protection products are an emerging technology aimed at enhancing the efficiency and sustainability of agricultural production. This paper presents the design and development of an innovative UAS-based spraying system for targeted and precise spraying applications in vineyards. The proposed solution introduces several innovations compared to state-of-the-art technologies. Nozzle positioning is optimized through Computational Fluid Dynamics simulations and wind tunnel tests to improve the control of sprayed droplets. The system reduces the sloshing effect, i.e. the oscillation of the onboard liquid, through a tank with internal baffles and with the adoption of a sloshing-aware control strategy. Precision spraying is further enhanced by a methodology for vineyard row tracking and following. Although the system is under development, this article outlines the architecture, the design methodology, and presents some preliminary results. A first prototype has been successfully developed and preliminarily tested, demonstrating the feasibility of the project and validating some of the key design concepts, while further developments are ongoing to implement all system functionalities.

Keywords: UAS Applications, Multirotor Design and Control, Autonomy
Abstract: Inspection and assessment of large concrete structures is the primary method to monitor their status and is conventionally conducted by trained inspectors and climbers with specialized equipment, which can be expensive and ineffective at times. Non-destructive inspection solutions emerge as a good candidate for facilitating such evaluations. In this article, we present a human supervisory control to fully automate both the approach and interaction stages of the measurement process employing a multirotor featuring tiltable rotors, characterized by a streamlined kinematic model. Our software seamlessly integrates with the PX4 autopilot firmware, thereby harnessing the complete capabilities of the flight controller. Furthermore, this integration enabled us to leverage all compatible tools such as QGroundControl, log review functionalities, and others, optimizing efficiency and functionality. Thanks to that, it has been possible to test our system without an experienced and/or certified drone pilot on the concrete pillars of a highway bridge, reducing the time and the costs needed to deploy and validate such a robot.

Keywords: UAS Applications, Perception and Cognition, Manned/Unmanned Aviation
Abstract: Due to the critical role of power lines in modern infrastructure, numerous automated methods have been developed for their inspection. Among these, Unmanned Aircraft Systems (UAS) have emerged as a valuable tool, offering rapid and precise inspections by capturing high-resolution aerial imagery of power lines. Drones enable access to hard-to-reach areas, reduce safety risks for workers near live wires, and significantly lower the time and cost associated with traditional inspection methods. In particular, deep learning techniques have been widely applied to automate the analysis of key components via the onboard camera. However, these methods typically rely on a first stage of detection based on large, annotated datasets focused on specific components, limiting their adaptability to new or unseen components. This paper investigates the application of two state-of-the-art algorithms of Few-Shot Object Detection (FSOD) for power line component detection: DeFRCN and CD-ViTO, alongside a modified Yolov8 detector of our own in which we integrated the modules of DeFRCN. We evaluate their performance using both public and proprietary datasets, analyzing unexpected outcomes and provide insights into the practical applicability of FSOD in real-world scenarios.

Keywords: UAS Applications, Regulations, Levels of Safety
Abstract: Beyond Visual Line of Sight (BVLOS) operations of Uncrewed Aerial Systems (UAS) hold significant potential for transforming many sectors, but face significant regulatory, safety, and operational complexity challenges. This paper presents a framework developed for planning and executing safe and compliant BVLOS missions in the context of wildlife conservation. While validated through a case study at the Ol Pejeta Conservancy, Kenya, this approach may also serve as a foundation for similar operations in other complex environments. Leveraging the widely adopted Specific Operations Risk Assessment (SORA), we developed an operational framework that addressed both air and ground risks. Key measures include strategic planning, coordination with local authorities, and the establishment of contingency volumes and operational procedures to ensure safety.

Field trials have demonstrated the practical challenges of ensuring airspace safety and highlighted the importance of close collaboration with Air Traffic Control (ATC) and the need for more robust, and redundant Command & Control (C2) solutions for long-range or remote operations. This study provides a replicable framework applicable to diverse BVLOS scenarios while offering insights specific to wildlife conservation. Documentation related to this work is publicly accessible: https://github.com/GuyMaalouf/WD-June24-BVLOS-Docs

Keywords: UAS Applications, Simulation, Perception and Cognition
Abstract: Employing hybrid aerial-amphibious drones, for activities in the marine environment, comes with a series of challenges that concern managing the interaction between the robot and the water surfaces, especially in the presence of waves. This paper focuses on the take-off transition from water to air, aiming to identify and predict those time windows in which the drone has a close to zero roll and pitch (horizontal attitude) and the propellers are the furthest from the water surface, thus optimizing the take-off. The proposed solution merges the measurement of the drone vertical displacement, due to the wavefronts, with the attitude data, returning a prediction signal that marks the best take-off windows in the immediate future. The idea has been validated through numerous simulated case studies.

Keywords: Risk Analysis, Levels of Safety, UAS Applications
Abstract: The advent of quadrotors has revolutionized applications such as surveillance, logistics, and disaster response, owing to their versatility and manoeuvrability. However, their nonlinear dynamics and sensitivity to actuator and propulsion failures pose significant safety risks. Additionally, the black-box nature of traditional artificial intelligence (AI) models hinders transparency and trustworthiness in safety-critical predictions. This paper presents a novel data-driven and explainable AI framework for predicting quadrotor crash areas under single actuator and complete power failure scenarios. The framework uses high-fidelity simulation data and the Feyn QLattice algorithm to model complex descent dynamics while offering an interpretable symbolic expression for external stakeholders. Comparative predictive evaluations with machine learning models, including random forests (RF) and extreme gradient boosting (XGB), reveal that the QLattice algorithm achieves competitive accuracy with RMSE and R2 values of 7.666 and 0.969, respectively. Validated through 5-fold cross-validation and hold-out testing, the framework demonstrates its potential to advance quadrotor safety by balancing accuracy, efficiency, and interpretability. Upcoming research will focus on integrating wind disturbances, investigating additional failure scenarios, and creating and refining interpretable algorithms to improve predictive performance.

Keywords: Energy Efficient UAS, Air Vehicle Operations, Autonomy
Abstract: This paper describes flight planning for a Vertical Take-Off and Landing (VTOL) QuadPlane small Uncrewed Aircraft System (sUAS). Five Lift+Cruise sUAS waypoint types are defined and used to construct smooth flight path geometries and acceleration profiles. Accelerated coverage flight plan segments for hover (Lift) and coverage (Cruise) waypoints are defined. Carrot-chasing guidance shows a trade-off between tracking accuracy and control stability as a function of carrot time step. Experimentally derived QuadPlane aerodynamic and thrust models for vertical, forward, and hybrid flight modes are developed as a function of airspeed and angle of attack. The QuadPlane feedback controller supports a novel hybrid mode that combines multicopter and aircraft actuators to add a controllable pitch degree of freedom at the cost of increased energy use. Energy aware coverage planning results show fly-coverage cruise waypoints are most efficient given long inter-waypoint distances. Energy versus coverage Pareto fronts analyze waypoint type tradeoffs for closely spaced waypoint cases.

Keywords: Autonomy, Control Architectures, Simulation
Abstract: A vast body of research exists on the guidance of autonomous unmanned aerial vehicles, with most approaches relying on geometric relationships and constant gains. While these methods can be optimized for predefined flight paths, they become suboptimal in dynamic scenarios requiring real-time guidance without prior knowledge, sharp turns, or significant variations in path length. This work introduces an optimal guidance algorithm with adaptive gains and inherent robustness to external disturbances. By defining the state weighting matrix as a function of cross-track errors, the proposed approach dynamically adjusts gains to minimize deviations. Additionally, incorporating an integral term into the state-space dynamic model ensures zero steady-state error. Lyapunov stability of the algorithm is demonstrated for all possible state weighting matrices. The algorithm is evaluated in a six-degree-of-freedom simulation environment and validated through real-world flight tests under high-wind conditions. Results demonstrate superior robustness and path-tracking performance compared to widely used proportional navigation methods, particularly in adverse environments.

Keywords: Regulations, Airspace Management, Air Vehicle Operations
Abstract: With the advance of communication technologies such as the 5G/6G and the widespread Internet of Things (IoT) in many application domains, the need for supporting infrastructure is becoming an important concern. Traditional solutions either do not meet the requirements or are becoming too expensive. In this context, an emerging approach based on High Altitude Platform Stations (HAPS) is revealing itself as a promising solution. However, the legal and regulatory frameworks necessary to enable their large-scale deployment remain fragmented or underdeveloped in most regions. Despite the technical advances regarding the design and deployment of these platforms, important concerns are raised in terms of legal framework to make it feasible. In light of this gap and observing the significance of the employment of these high-altitude unmanned platforms, this work provides a discussion on regulatory aspects involved in HAPS operation, with a particular focus on the recent advances in Brazil and in Europe. Finally, a prospective analysis of the steps that are coming in HAPS regulation is provided.

Keywords: Regulations, Manned/Unmanned Aviation
Abstract: In recent years, Unmanned Aerial Systems (UAS) have surged in popularity, promising transformative applications across diverse sectors. Nevertheless, their rapid and widespread adoption poses significant challenges in the realm of regulatory frameworks. This study undertakes a comprehensive examination of the legal norms pertaining to UAS at the European Union (EU) level, followed by a comparative analysis of domestic regulations in selected countries. The primary objectives are to meticulously assess these norms, identify commonalities, and delineate disparities. The findings unveil shared aspects, as well as divergent approaches to UAS regulation among individual member countries, accentuating the pressing necessity for enhanced harmonization of regulations at the EU level. Such an endeavor provides a profound understanding of the legal landscape surrounding UAS, ultimately contributing to the responsible integration of this technology across all sectors. In the complex and evolving skies of UAS regulation, this research serves as a guiding star, illuminating the path towards a unified and effective legal framework.

Keywords: Regulations, Manned/Unmanned Aviation
Abstract: The article examines the Fourth Amendment to the United States Constitution, which guarantees protection against unreasonable searches and seizures. This is a foundation of the right to privacy for citizens. However, with the emergence of modern technologies such as drones, interpreting the regulations regarding the protection of citizens may encounter challenges. The article examines the impact of drones on the right to privacy in the context of their use by law enforcement and private entities. The author presents key court decisions and current regulations governing the use of drones in public spaces. In addition, she draws attention to the conflicts between the public interest and the privacy of citizens, as well as the need to create clear legal regulations. The article also indicates that the dynamic development of technology requires a new interpretation of the Fourth Amendment to effectively protect the right to privacy in the 21st century.

Keywords: Risk Analysis, Levels of Safety, Path Planning
Abstract: The increasing use of Unmanned Aircraft Systems (UAS) in critical scenarios raises the need for efficient mission planning and risk assessment methodologies. One of the main challenges in UAS operations is the regulatory approval process, which requires operators to prepare comprehensive and compliant documentation. In this paper, we propose a mission planning methodology designed to assist UAS operators in conducting a risk assessment aligned with SORA 2.5 and generating documentation that meets European regulatory requirements. Additionally, our methodology integrates a risk-aware path planning approach to compute low-risk flight routes. Another key aspect of our approach is a situational awareness module, which enables real-time monitoring of risk and operational constraints during mission execution. If the initially planned route becomes unsafe, a re-planning algorithm dynamically adjusts the route, ensuring an adequate level of safety. Although the proposed methodology is still under development, this paper presents the essential requirements and features, as well as preliminary results on risk map generation and risk-based route planning and re-planning algorithms.

Keywords: Control Architectures, Navigation, Reliability of UAS
Abstract: This study introduces a control strategy for Unmanned Aerial Vehicles (UAVs) performing high-precision proximity tasks in restricted tunnel environments, enabling them to conduct detailed inspections and navigate through extremely narrow tunnel corridors. The primary challenge in these tasks lies in managing nonlinear aerodynamic forces and torques induced by the tunnel walls while ensuring safe and efficient UAV operation in close proximity to these boundaries. To tackle this issue, we propose a novel approach that integrates Model Predictive Control (MPC) with modified Control Barrier Function (CBF) constraints. This framework is designed to achieve dual objectives: ensuring a safe operational distance from walls to mitigate their aerodynamic effects, while simultaneously minimizing distance to the walls to effectively perform close-proximity operations. Our approach demonstrates significant improvements, reducing the safe hovering distance from walls by 37% and decreasing UAV power consumption by approximately 15% when flying near ground and ceiling surfaces. The efficacy of the proposed method is rigorously validated through comprehensive simulations, which evaluate various close-proximity UAV trajectories under realistically modeled aerodynamic disturbances induced by the tunnel boundaries.

Keywords: Control Architectures, Path Planning, Navigation
Abstract: This paper presents a general MPC-based control framework that includes the linear complementarity problem (LCP) for modeling the interaction forces of a mobile robot. To validate our approach, two case studies are considered: (i) an aerial robot that should reach a target point placed on a frictionless surface; and (ii) an aerial robot that should lift a cable-suspended mass, switching from a slack to a taut cable condition. The simulation results confirm the validity of our approach, and the ability of the LCP to model the interaction forces for an aerial platform.

Keywords: Control Architectures, See-and-avoid Systems, Swarms
Abstract: This article proposes a repulsive vector field-based collision avoidance for quadrotors performing position regulation tasks. The proposed strategy is evaluated by employing commercial drones that can be controlled through the body frame's translational velocity. The collision avoidance algorithm activates after a threshold distance between drones is exceeded. The regulation controller and collision avoidance strategy are experimentally evaluated when two drones switch their position in such a way that they cross each other close to the origin.

Keywords: Security, Control Architectures, Integration
Abstract: Unmanned aerial vehicles (UAVs) depend on untrusted software components to automate dangerous or critical missions, making them a desirable target for attacks. Some work has been done to prevent an attacker who has either compromised a ground control station or parts of a UAV's software from sabotaging the vehicle, but not both. We present an architecture running a UAV software stack with runtime monitoring and seL4-based software isolation that prevents attackers from both exploiting software bugs and stealthy attacks. Our architecture retrofits legacy UAVs and secures the popular MAVLink protocol, making wide adoption possible.

Keywords: Swarms, Autonomy, Control Architectures
Abstract: Deploying decentralized control strategies for outdoor multi-agent Uncrewed Aircraft Systems (UASs) is challenging due to timing variations, packet loss, and computing resource limitations. In this work we address robustness to these conditions through a novel co-regulated control strategy that varies the periodicity of control inputs and communication with other agents. Co-regulation is applied to a decentralized hierarchical controller consisting of a global component governing inter-group coordination to multiple targets while a local component governs intra-group coordination of the agents as they progress to the target of interest. The control gains are ``gain scheduled'' according to current conditions while a cyber controller schedules the control and communication tasks for execution based on swarm performance. The control gains are found via reinforcement learning and the entire algorithm is deployed on a swarm consisting of 7 custom agents. Our results show the impact of rethinking swarming algorithms with computation and communication resource limitations in mind and indicate we can provide exceptional swarm control utilizing fewer resources while also improving the quality of service or an onboard, anytime collision avoidance algorithm.

Keywords: Swarms, Navigation, Control Architectures
Abstract: Maintaining the formation of complex structures with multiple UAVs and achieving complex trajectories remains a major challenge. This work presents an algorithm for implementing flocking behavior of UAVs based on the concept of Virtual Centroid to easily develop a structure for the flock. The approach builds on the classical virtual-based behavior, providing a theoretical framework for incorporating enhancements to dynamically control both the number of agents and the formation of the structure. Simulation tests and real-world experiments were conducted, demonstrating its simplicity even with complex formations and complex trajectories.

Keywords: Security, Navigation, Risk Analysis
Abstract: The integration of GNSS with UAVs enhances their ability to perform high-precision geospatial. However, as UAVs become increasingly reliant on these systems as the core of their navigation and operational capabilities, they are also more susceptible to GPS spoofing attacks. These attacks manipulate or counterfeit positioning signals and data, leading to navigation errors and potential mission failures. This paper presents a systematic literature review (SLR) aimed at categorizing and analyzing state-of-the-art GPS spoofing methods, tools, tests, and techniques, with the goal of enhancing the understanding of both the spoofing process and the systems involved in its execution. The study explores the classification of GPS spoofing attacks, their implementation, and the hardware/software tools used for both conducting and detecting them. Additionally, it reviews existing countermeasures and highlights critical challenges in GPS security research, such as the necessity for real-world validation, implementation costs, and the growing complexity of both attacks and detection techniques. By consolidating recent advancements, this review provides a structured reference for researchers and practitioners, supporting the development of more effective detection and mitigation strategies against GPS spoofing threats.

Keywords: Security, Networked Swarms, Swarms
Abstract: Unmanned Aerial Vehicles (UAVs) have been gaining popularity in various areas, such as military, civil and commercial. However, these vehicles are exposed to cyber threats that can compromise their security and privacy and even result in physical damage. Such threats include signal interception, unauthorized access, data theft, and even remote control of the UAV. Therefore, UAV manufacturers and users need to be aware of these threats and adopt appropriate measures to strengthen the security and integrity of the systems. One of the defense strategies is the implementation of an Intrusion Detection System (IDS), which monitors the system for suspicious behavior. When an abnormality is identified, the IDS sends a notification to the control station, allowing appropriate decision-making. IDS aimed at UAVs often focus on detecting attacks on specific data sources, without considering the application in a group scenario. In this context, this paper presents a collaborative intrusion detection system for group security of UAVs. The system is capable of identifying threats both on the network and in-flight, using supervised and unsupervised learning. Attacks detected on the network include blackhole, grayhole, and flooding, while in-flight threats include GPS spoofing and jamming, with tests carried out using real UAVs. Federated learning is incorporated into the system to preserve data privacy and promote collaboration in training between UAVs. In addition, geographic and physical characteristics are considered to ensure that the IDS operates independently of the specific hardware of the UAVs. The development also focuses on implementing a lightweight IDS, ensuring efficiency and optimized operation.

Keywords: Security, Simulation
Abstract: This paper proposes a method to model and analyze the performance of a Counter-Drone System (CDS) using Bayesian Networks (BN). Quantitative performance indexes related to the sensor and the tracking algorithm used in the CDS are proposed. They are used in a BN which also accounts for CDS functions related to alert, localization and engagement of neutralization means. A case study is proposed to illustrate how performance of the CDS can be evaluated under various scenario conditions, including different types of drones and influences of the environment. To illustrate how the BN can also help for design considerations of a CDS, influence of the sensor location is also analyzed.

Keywords: Security, UAS Applications, Technology Challenges
Abstract: Unmanned aerial vehicles (UAV), or drones, offer immense potential but also pose major security concerns due to their accessibility and misuse. Therefore, effective drone detection and identification are crucial to mitigate these risks. This study explores the application of different preprocessing techniques and deep learning models for the identification of drones and the detection of unknown drones by their acoustic signature. Specifically, We study the effectiveness of using a Short-Time Fourier Transform (STFT)-based approaches in generating audio spectrograms for deep learning multi-class classification. Our findings demonstrate the efficacy of deep learning models in achieving promising results for the audio identification of drones. We focus on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Convolutional Recurrent Neural Networks (CRNN), analyze the performance of each for STFT spectrograms. STFT spectrograms consistently offer the best overall classification results. In conclusion, this analysis compares the varying potential of utilizing different acoustic features and deep learning algorithms for accurate, real-time UAV identification.

Keywords: Swarms, Autonomy, Path Planning
Abstract: We present a framework for space partitioning, where the Regions of Influence (ROIs) of the agents are defined based on proximity metrics derived from the cost of optimal control problems. Efficient space partitioning in multi-agent systems, particularly in Unmanned Aerial Vehicle (UAV) operations, is critical for coverage, load balancing, and task allocation. However, traditional methods, such as the standard Voronoi Diagrams (VDs) based solely on distances, often fail to account for the dynamic behavior and capabilities of UAVs. We generalize the VD concept by replacing distance-based metrics with transition costs obtained from optimal control formulations. This allows the resulting partitions to incorporate UAV dynamics, including initial states and control effort, in defining regions where one agent is more suitable than another for a given task. We show that for a broad class of problems with second-order optimal costs, the boundaries between ROIs are given by either hyperplanes or quadratic surfaces. This includes, as special cases, classical VDs based on distance, minimum-time problems for single integrators, the fixed-final-state (FFS) optimal transfer problem, and Linear Quadratic Regulators (LQR). Overall, the proposed framework bridges geometric and control-theoretic space partitioning, enabling dynamic and context-aware task allocation in multi-agent systems.

Keywords: Swarms, Path Planning, Simulation
Abstract: Due to climate change in recent years, wildfires have become one of the most harmful hazards to the environment and society. In firefighting operations, the early stages are crucial to controlling wildfires successfully. In this paper, we propose an improvement to an existing dynamic space partition (DSP) algorithm by adding an Archimedean spiral to enable wildfire detection in large areas on the scale of California. Compared to the baseline DSP controller, the improved algorithm provides more efficient area coverage with the same number of robots in the simulation. With a swarm of 30 robots, the DSP algorithm with an Archimedean spiral (DSP-A) can identify 87.81% static fires. With the same configuration, the baseline DSP algorithm covered 79.77% of total fires. Furthermore, the DSP-A controller is resilient when the number of robots decreases. When the number of robots in the swarm drops from 30 to 10, the DSP-A algorithm can still cover 70% of wildfires, while the performance of the baseline DSP controller is reduced to 44%.

Keywords: Control Architectures, Simulation
Abstract: Flapping-wing flying robots (FWFRs) are becoming a trend case study in the control field. The model of an FWFR in the gliding phase of flight is similar to an unmanned lightweight aircraft. By actuating the wings (flapping), a periodic motion disturbs the dynamics and, additionally, generates the lift and thrust forces. The actuation makes the traditional analytical dynamics complex and computationally heavy for simulations of control algorithms. In this work, the vertical dynamic of the FWFR is presented using the equivalent dynamic approach as forced base excitation. Then, a wind disturbance model is implemented to study the effect of wind gusts. The state-dependent Riccati equation (SDRE) method is applied to control the model for height regulation, exploiting its nonlinear-optimal capabilities on the nonlinear FWFR model and evaluating the system response to the wind disturbance. The SDRE results were compared with the linear quadratic regulator (LQR) controller. The SDRE mimics the LQR design and delivers a nonlinear version; hence, the LQR is a good candidate for comparing the results.

Keywords: Control Architectures, Multirotor Design and Control, UAS Applications
Abstract: This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

Keywords: Autonomy, Perception and Cognition, UAS Applications
Abstract: Accurate and robust vision-based pose estimation is essential for cooperative unmanned aerial vehicle (UAV) operations, particularly in formation flight and multi-UAV coordination, where precise relative positioning is critical to mission success. However, many existing systems rely on active sensors, limiting their applicability in environments with communication constraints, GNSS denial, or stealth requirements. To overcome these limitations, recent studies have explored the use of passive sensors such as cameras. However, current methods, including marker-based and learning-based approaches, perform well under controlled conditions, but often struggle with viewpoint variability during dynamic maneuvers. To address these challenges, this paper presents the Viewpoint-Aware Pose Estimation (VAPE) framework, which enhances robustness across diverse viewpoints while operating with passive vision sensors. VAPE integrates viewpoint classification, robust feature matching using pre-trained models, and spatial feature distribution analysis to establish accurate 2D-3D correspondences without the need for specialized markers or extensive feature annotation. Ground tests simulating formation maneuvers demonstrate that VAPE maintains reliable tracking performance, achieving mean absolute position errors below 2.5% and angular errors below 5°, indicating its potential for real-world UAV coordination tasks.

Keywords: Fail-Safe Systems, UAS Applications
Abstract: This paper presents a concept for automatic identification of landing spots for safety landings applied for unmanned aerial vehicles (UAVs) with vertical take-off and landing (VTOL) capabilities. It utilizes open-source geodata, consisting of land use data and an elevation model. With the growing deployment of UAVs across diverse applications, it is necessary to ensure operational safety in emergency situations. In such situations, the flight system must be able to safely abort its current flight mission by landing at a so-called safety landing point and thus minimize both air and ground risk. A systematic procedure for determining potential safety landing points using two data sources (elevation model and land use data) based on predefined criteria has been developed. The presented results demonstrate the effectiveness of using open-source data in UAV operations, thereby paving the way for more robust and safe flight operations in various environments.

Keywords: Navigation, UAS Applications, Path Planning
Abstract: During the last two decades, Unmanned Aerial Vehicles (UAVs) have been employed for a wide range of civil and public domain applications, as well as in missions to Mars. In complex autonomous exploration scenarios, particularly in GPS-denied environments, the software integrated into the Guidance, Navigation, and Control (GNC) systems plays a critical role in ensuring UAV stability and autonomy. To meet these requirements and address the limitations of traditional navigation techniques, the development of Deep Reinforcement Learning (DRL) approaches to support decision-making tasks has gained significant traction in recent years. The goal of the paper is twofold: i) to present a comparison between the traditional Proximal Policy Optimization (PPO) and the augmented PPO with a transformer architecture, ii) to achieve smooth and efficient trajectories by designing a continuous physics informed reward function accounting for the Least Action Principle (LAP). The results demonstrate that PPO achieves significantly improved performance when integrated with the transformer, as well as high efficiency of the trained agent when simulating a specific flight path. This enhancement highlights the potential of transformer-based architectures to more effectively address complex decision-making tasks than traditional DRL methods.

Keywords: Energy Efficient UAS, Micro- and Mini- UAS, UAS Applications
Abstract: Dynamic soaring is a method used by seabirds or small aircraft to harvest energy from a wind gradient. This work shows an adaptive dynamic soaring controller algorithm implemented in a software-in-the-loop simulation of a common autopilot flight stack, commanding only the pitch rate, roll rate, and throttle setpoints. The algorithm smoothly transitions from low-wind to high-wind velocity gradients, using the aircraft propulsion system only as necessary, up to the point that the propulsion system is not employed given sufficient wind. Using this algorithm, a set of four unique fixed-wing UAVs are demonstrated to perform dynamic soaring in an oceanic environment simulation, showing the potential for energy-augmentation and unlimited cross-ocean flight in small fixed-wing UAVs.

Keywords: Autonomy, Control Architectures, Path Planning
Abstract: Uncrewed Aerial Vehicles (UAVs) are increasingly applied across various fields due to their strong mobility and high flexibility. Concurrently, the rapid development of Artificial Intelligence (AI) has unlocked new potentials for autonomous learning and the evolution of robots. This synergy enables UAVs equipped with AI capabilities to perform complex tasks such as real-time path planning and swarm management more adeptly than traditional models reliant on pre-programmed algorithms. This paper builds on our previously proposed deep reinforcement learning and fuzzy logic-based multiple UAV dynamic target interception algorithm, introducing several improvements and innovations aimed at safe applications in the real world. Initially, several components of the original algorithm have been redesigned and improved; subsequently, an inter-platform simulation environment incorporating MATLAB, ROS, PX4 has been established. Finally, a programmable drone has been constructed. The improved algorithm has been validated through systematic phases of simulations and actual flight tests under complex and dynamic conditions, establishing a solid link from algorithm design to practical applications.

Keywords: Environmental Issues, Technology Challenges, Aerial Robotic Manipulation
Abstract: Unmanned Aerial Vehicles (UAVs) provide many benefits and opportunities across a range of sectors, including surveillance, humanitarian work, disaster management, research, and transportation. Due to their accessibility and affordability, they are now used more than ever, which also poses some challenges. This is the noise pollution produced by the motors and propellers that has been highlighted as a significant issue to the people’s health and the environment. To address this issue, this paper proposes to use Generative Adversarial Networks (GAN) to produce an inverse sound signal based on the drone’s acoustic signals and use that to cancel the noise produced by the drone. We synthesize training data spanning the acoustic diversity of drone noise: steady-state propeller tones, rapid throttle transitions (simulating ascent/descent), and superimposed broadband turbulence. The GAN model is capable of adapting to dynamic settings, learning from data, and adjusting to testing conditions accordingly. We compared our proposed solution with other techniques that can also be used for drone signal interference in order to suppress the drone noise. This research idea paves the way for the need to address the issue created due to drone noise and a solution in managing this problem for modern drone applications.

Keywords: Autonomy, Control Architectures, UAS Applications
Abstract: In this article, we present a hierarchical supervisory reinforcement learning (RL) framework for achieving precise trajectory tracking of UAV(s) operating in dynamic and complex environments. The UAV is equipped with multiple controllers, each controller tuned to provide a desired response under specific environmental conditions. Our objective is to dynamically schedule these controllers in response to abrupt environmental changes. To this end, we develop an RL-based framework for adaptive controller scheduling. We derive sufficient conditions for switching stability and validate our approach through extensive numerical simulations.

Keywords: Autonomy, Environmental Issues, UAS Applications
Abstract: Precise tree measurement is essential for forest inventory and biomass estimation. Current methods often capture data only from the upper or lower parts of trees, resulting in incomplete or estimated measurement, bringing uncertainty to measurements. Some approaches fuse upper and lower tree data, but they require the alignment and merging of dense point clouds, making large-scale implementation challenging. This study presents an autonomous navigation and real-time data extraction method using an unmanned aerial vehicle (UAV) equipped with depth cameras and a LiDAR sensor. The system navigates autonomously and maps the environment around it, using a uniform voxel grid to segment and measure individual trees. The results demonstrated that: (1) the UAV successfully navigates autonomously between trees, mapping the unstructured and unknown environment while performing real-time reconstruction; (2) tree trunk segmentation, measurement, and localization were achieved with an root mean square error (RMSE) of 0.22 m; and (3) tree height measurements obtained an RMSE of 0.05 m. The proposed methodology proved to be effective for forest inventory applications, providing accurate tree measurements with improved computational efficiency.

Keywords: Autonomy, Navigation
Abstract: In this paper we address the problem of ‘weaponeering’, i.e., placing the weapon engagement zone (WEZ) of a vehicle on a moving target, while simultaneously avoiding the target’s WEZ. A WEZ describes the lethality region of a range-limited weapon considering both the range of the weapon along with the state of the target. The weapons are assumed to have simple motion, while the vehicles carrying the weapons are modeled with Dubins dynamics. Three scenarios are investigated and are differentiated in the assumptions that can be made about the target in the process of the vehicle control design: 1) no knowledge of target control, 2) avoid unsafe positions assuming the target’s optimal control, 3) full knowledge of target’s optimal control. The engagement is formulated as a stochastic optimal control problem with uncertainty in the target’s control modeled using a noise parameter applied to the target’s control input. After discretizing the Hamilton-Jacobi-Bellman equation, Value iteration is then used to obtain an approximate solution for the optimal vehicle control and time-to-go. Simulation results support usage of the first paradigm: assume no knowledge of the target’s control.

Keywords: Autonomy, Path Planning, Technology Challenges
Abstract: This paper presents the development of an Artificial Intelligence (AI) based fighter jet agent within a customized Pygame simulation environment, designed to solve multi-objective tasks via deep reinforcement learning (DRL). The jet's primary objectives include efficiently navigating the environment, reaching a target, and selectively engaging or evading an enemy. A reward function balances these goals while optimized hyperparameters enhance learning efficiency. Results show more than 80% task completion rate, demonstrating effective decision-making. To enhance transparency, the jet's action choices are analyzed by comparing the rewards of the actual chosen action (factual action) with those of alternate actions (counterfactual actions), providing insights into the decision-making rationale. This study illustrates DRL's potential for multi-objective problem-solving with explainable AI.

Keywords: Airspace Control, Autonomy, Manned/Unmanned Aviation
Abstract: This paper presents a monotonically weighted nonlinear model predictive control (NMPC) strategy for dynamics-driven visual servoing of an over-actuated quadrotor. The proposed control framework incorporates a dynamics-driven formulation that explicitly accounts for the multirotor’s over-actuated nature, enabling precise trajectory tracking and robust disturbance rejection. A key innovation is the introduction of a monotonically weighted cost function, which eliminates the need for terminal constraints while ensuring stability and computational efficiency. Additionally, an adaptive prediction horizon mechanism is developed to dynamically adjust the control horizon, enhancing real-time feasibility without compromising control performance. To evaluate the effectiveness of the proposed approach, four distinct maneuvering scenarios are considered, including pure translation, translation with rolling, translation with pitching, and full six-degree-of-freedom motion. Comparative simulations demonstrate that the proposed NMPC achieves improved tracking accuracy and reduced computational latency compared to state-of-the-art Tube MPC and Adaptive MPC approaches.

Keywords: Airspace Control, Path Planning, Payloads
Abstract: Abstract— Increasing the load capacity of drones can be ef- fectively achieved by utilizing two drones. This paper introduces a novel cooperative control strategy for the transport of a slung bar-shaped load using two drones. Our approach integrates differential flatness-based motion planning with filtering tech- niques to generate reference trajectories. This method does not require prior knowledge of the load mass or cable deflection angles, and ensuring compatibility with a wide range of flight controllers. The proposed control scheme employs a super- twisting controller as an internal stabilizer to ensure robustness against model inaccuracies and external disturbances, providing precise stabilization around the preplanned trajectories. This methodology is particularly well-suited for practical applica- tions requiring the cooperative transport of complex loads, such as wind turbine blades, where precision, robustness, and simplicity are critical.

Keywords: Airspace Control, Reliability of UAS, Risk Analysis
Abstract: With the imminent integration of Advanced Air Mobility (AAM) into the national airspace, ensuring the robustness of flight controllers in spatially congested metropolitan areas and in the presence of external disturbances is of paramount importance. The complex interaction between atmospheric turbulence and tall buildings further exacerbates the effects of wind disturbances, posing significant safety challenges to aircraft stability and trajectory tracking. This study employs the high-fidelity urban wind field model using Computational Fluid Dynamics (CFD) which captures wind variations in urban environments. This model quantifies wind shear intensity and vorticity distributions, which are critical factors affecting flight performance. The failure of an autonomous fixed-wing aircraft to maintain its intended flight path within permissible deviation limits under extreme wind conditions is investigated. To address these challenges, a robust flight control system is developed to enhance trajectory tracking performance and mitigate the adverse effects of wind on the path following. The proposed controller is designed to ensure reliable operation despite the unpredictability of urban wind fields, contributing to safer and more resilient autonomous flight operations in complex metropolitan airspaces.

Keywords: Navigation, Path Planning, Swarms
Abstract: In search and rescue (SAR) operations, rapid and comprehensive exploration of unknown indoor environments is critical for locating survivors and assessing structural integrity. This paper presents a novel multi-unmanned aerial vehicle (UAV) framework for autonomous exploration in GPS-deprived indoor environments, leveraging advanced sensing technologies and algorithmic strategies. The proposed methodology integrates LiDAR and 3D simultaneous localization and mapping (SLAM) for real-time environment reconstruction, coupled with a weighted frontier-based exploration strategy and Dijkstra’s algorithm for collision-free path planning. This combination enables UAVs to prioritize unexplored regions systematically while minimizing redundant coverage. The system’s efficacy was validated through high-fidelity simulations in RViz and Gazebo, replicating multi-floor damaged buildings. Performance metrics, including total travel distance and percentage of unvisited areas, demonstrate the framework’s ability to achieve nearcomplete 3D coverage (exceeding 90% in tested scenarios) while significantly reducing exploration time compared to manual methods. These results highlight the framework’s potential to enhance the safety and efficiency of SAR missions by reducing human exposure to hazardous environments and accelerating critical decision-making.

Keywords: Networked Swarms, Path Planning, Swarms
Abstract: The paper develops a deep neural network- (DNN-) based mass transport approach to cover a distributed target in a decentralized manner by Uncrewed Aerial Systems (UAS). This is a new decentralized UAS transport approach with time-varying communication weights that can be achieved by solving the following three sub-problems: (i) determining the DNN structure, (ii) obtaining communication weights, and (iii) ensuring stability and convergence guarantee. By proposing a novel algorithmic approach, the DNN is structured based on the agent team initial formation with an arbitrary distribution in the motion space. To specify communication weights for a team of 𝑁 multicopters, we use the DNN to obtain the initial communication weights, based on the agents’ initial positions, abstractly represent the distributed target by 𝑁 points, considered as the final positions of all agents, and obtain the final communication weights. The third sub-problem is to prove the stability and convergence of the UAS transport.

Keywords: See-and-avoid Systems, Path Planning, Autonomy
Abstract: This paper presents an innovative collision avoidance and path planning framework for unmanned aerial vehicles (UAVs) using minimal camera-based inputs. The system leverages visual data to predict the future trajectories of nearby flying objects and compute low collision risk paths while maintaining progress toward designated targets. This solution extracts only two essential parameters from the visual feed--bearing and pixel area--enabling practical obstacle detection and avoidance. Furthermore, our approach avoids the target observability problem without relying on extensive ownship maneuvers, allowing collision avoidance with minimal movement. Designed for UAVs operating in shared airspace with manned aircraft, the proposed framework emphasizes autonomous decision-making to improve operational safety. Simulation results demonstrate the system's capability to effectively plan avoidance maneuvers and generate feasible routes in complex and dynamic environments.