Unmanned Aerial Vehicles (UAVs) require reliable and adaptable autonomy frameworks to perform critical tasks such as infrastructure inspection, where safety, mission adaptability, and real-time responsiveness are of prime importance. This paper presents a modular framework for autonomous inspection missions using Behavior Trees (BTs) which can be applied to heterogeneous UAVs. This framework builds on prior efforts to integrate customized commercial UAVs within an open-source system, ensuring adaptability across various mission requirements. By leveraging BTs within the Robotic Operating System (ROS) framework, the architecture emphasizes modularity, scalability and real-time adaptability, overcoming the rigidity of traditional state machine approaches. A clear separation between decision-making and actuation is maintained, with BTs dedicated to execute well-defined tasks and responding to emergencies, whereas high-level decision logic is managed externally. The system supports essential mission tasks while incorporating robust emergency detection and response mechanisms to enhance operational safety. We validated this system in both simulated and real-world scenarios, demonstrating improved mission adaptability and safety across various inspection applications.

Highlights What are the main findings? The design, development, and validation of U-Plan, an integrated framework for the coordination, planning, real-time monitoring, and replanning of multiple heterogeneous unmanned aerial systems (UASs) that are capable of coping with real-world operational conditions, including UAS kinematic constraints, wind effects and airspace constraints, and dynamic changes in the UAS, mission, and environment conditions. What is the implication of the main finding? The validation of U-Plan and its components, integrated with professional-grade Visionair Ground Control Station (GCS) software and the VECTOR-SIL software-in-the-loop autopilot simulator, and a comparison with the main existing methods show significant efficacy, efficiency, scalability, and real-time adaptability to dynamic changes.Highlights What are the main findings? The design, development, and validation of U-Plan, an integrated framework for the coordination, planning, real-time monitoring, and replanning of multiple heterogeneous unmanned aerial systems (UASs) that are capable of coping with real-world operational conditions, including UAS kinematic constraints, wind effects and airspace constraints, and dynamic changes in the UAS, mission, and environment conditions. What is the implication of the main finding? The validation of U-Plan and its components, integrated with professional-grade Visionair Ground Control Station (GCS) software and the VECTOR-SIL software-in-the-loop autopilot simulator, and a comparison with the main existing methods show significant efficacy, efficiency, scalability, and real-time adaptability to dynamic changes.Abstract Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs' arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan's capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios.

Flapping wing Micro Air Vehicles (FWMAVs) hold great potential for real-world applications but are currently still hard to model. In this article, a simplified analysis of the equilibrium state of a tailless FWMAV in forward flight is presented. The definition of the equilibrium state complements previous dynamic and stability analysis, adding new information about the flight behavior of FWMAVs. A new aerodynamic decoupled model has been used for the analysis, considering separately the thrust force generated by the flapping movement and the lift and drag caused by the forward velocity. The aerodynamic forces are included in a dynamic model of the FWMAV, and the equilibrium state is derived. The formulation obtained is explicit in terms of the pitch actuator deflection, thus allowing its use for control corrections, and provides an estimation of the flight velocity. The thrust needed to maintain height is also formulated, demonstrating that forward flight is more efficient than hovering. The results are validated experimentally for the pitch angle, showing good agreement with the analytical results. Then, the dynamics of the FWMAV are simulated, comparing the results with experiments where the FWMAV goes from hovering to a specific pitch reference while maintaining its height. Additional simulations are performed with basic control considerations, showing how considering the equilibrium state for a feed-forward control significantly improves the flight behavior compared to PI and PID controllers, reducing the convergence time.

Aerial robots are transitioning from traditional surveillance and monitoring roles to more advanced tasks involving physical interaction. Despite this progress, physical Human-Aerial Robot Interaction remains largely underexplored due to the complexity and stability-related issues of such platforms. This paper introduces a novel control framework that enables an aerial platform to cooperatively transport an object with a human operator. The control approach is built on a nonlinear model predictive control (NMPC), integrating the dynamic models of the human, the aerial robot, and the transported object. To ensure safe and robust physical interaction, the NMPC is combined with a compliant controller. Additionally, our controller prioritizes forward motion over lateral movements to accommodate the human's natural direction of motion. We validate this framework through indoor flight experiments, demonstrating how a human operator and a fully actuated hexarotor can effectively collaborate to transport a bar. The results highlight the aerial robot's ability to assist the human during physical transportation tasks, enhancing efficiency and comfort.

Robotic grippers are increasingly deployed across industrial, collaborative, and aerial platforms, where each embodiment imposes distinct mechanical, energetic, and operational constraints. Established YCB and NIST benchmarks quantify grasp success, force, or timing on a single platform, but do not evaluate cross-embodiment transferability or energy-aware performance - essential for modern mobile and aerial manipulation. This letter introduces the Cross-Embodiment Gripper Benchmark (CEGB), a reproducible benchmarking suite extending YCB and selected NIST metrics with three additional components: a transfer-time benchmark measuring embodiment exchange effort, an energy-consumption benchmark evaluating grasping and holding efficiency, and an intent-specific ideal payload assessment. Together, these metrics characterize grasp performance and cross-platform suitability. CEGB is validated on two mechanically distinct grippers. The experimental evaluation quantifies embodiment-dependent differences in transfer time, energetic efficiency, and operational capability under unified statistical reporting. CEGB provides a reproducible foundation for cross-platform, energy-aware gripper evaluation.

Robots with aerial-ground locomotion and manipulation capabilities are being introduced as a promising solution to increase energy saving and manipulation accuracy for activities like inspection and maintenance in industrial environments. However, the effective operation of hybrid robots in these scenarios can become complex and higher levels of robot autonomy are still needed. This paper presents a novel motion planning method that exploits the hybrid locomotion capabilities of hybrid robots to compute safe and efficient paths with velocity references between inspection points in cluttered environments. The method, which is built over the basis of the RRT* algorithm, integrates the hybrid nature of these aerial-ground robots into the planning process to generate optimal global paths according to different indices (operation time, power consumption). Moreover, the motion planner also includes Dynamics Awareness for robust robot operation and collision avoidance in highly-cluttered environments. This consists of an extension of the planning algorithm to integrate the dynamic behaviour of the controlled robot during the expansion of the search tree. The method has been applied to the inspection of pipe arrays in industrial scenarios like oil and gas refineries. Validation results where the execution of hybrid paths generated by the motion planner has been evaluated through simulation under different conditions have demonstrated the clear advantages of this kind of hybrid planning.

This paper presents a novel computational framework for the design of closed-loop guidance and control laws in highly uncertain environments, possibly characterized by unmodeled dynamics and external disturbances. The proposed methodology integrates the use of Gaussian process regression (GPR) to model uncertainties within a stochastic optimal control framework. GPR provides a probabilistic approach to disturbance estimation using simulated or observed data to characterize noise as a Gaussian process. Convexification techniques are used to transform the original nonconvex stochastic control problem into a sequence of deterministic convex problems that can be efficiently solved by state-of-the-art interior-point algorithms. As a study case, the docking maneuver between an active chaser spacecraft and a passive target spacecraft in the presence of differential drag, with uncertainty in the ballistic coefficient, is considered. The obtained numerical results suggest the effectiveness of the proposed approach in compensating for the disturbances and driving the system to the target final distribution.

Doctoral Networks (DNs) aim to address systemic challenges in doctoral education, such as fostering interdisciplinarity, enabling international and intersectoral collaboration, enhancing employability, and promoting responsible innovation. While cohort-based training helps mitigate student isolation through workshops and summer schools, traditional DNs often struggle to fully realize their collaborative potential, often relying on predefined supervisor relationships or the initiative of individual researchers. In contrast, the Marie Sk & lstrok;odowska-Curie Actions (MSCA) Robotics and AI for Critical Asset Monitoring (RAICAM) DN was designed to maximize doctoral candidate (DC) collaboration and networking through a cohort-wide research challenge, requiring them to balance independent research with contributions to a shared, mission-driven objective. This study examines how structured training, including digital communities, application-focused research sprints, training schools, a robotics hackathon and a final demonstration enhances system integration and collaboration within the network. DCs located across seven European countries worked in virtual teams, refining systems through structured workflows, weekly meetings, and shared workspaces before training schools. Through continuous online collaboration and targeted sprints, RAICAM facilitated interdisciplinary integration. Two research sprints, conducted in Italy and France, and a robotics hackathon held in Austria, enabled teams to develop and test solutions for real-world challenges through an impact-driven plan that considers a given problem from an end-to-end perspective that requires and foster interdisciplinary collaboration. The results highlight the effectiveness of structured training in enhancing collaboration and adaptability, while identifying key areas for improvement. This study translates lessons from RAICAM into practical guidelines for future doctoral networks, demonstrating how structured training empowers students to drive interdisciplinary research independently.

This paper describes the design, development, and validation of a 40 by 40 cm size, 2.2 kg weight autonomous aerial delivery robot intended to conduct intra-logistics operations with light parcels (under 250 grams) that can be safely delivered directly to the people on flight. The aerial robot is fed by power cable to overcome the limitations of batteries while increasing the payload-to-weight ratio, covering the propellers with the carbon fiber frame structure and foam protection to avoid the potential damage in case of collision with the people or the environment. A comparison of the proposed design with respect to our previous prototypes is presented along with the related works to motivate the design choices. The aerial robot implements and compares two onboard localization and mapping methods: RTAB-Map with RGB-D camera and 2D LiDAR, and FAST-LIO2 with 3D LiDAR. The platform is validated in two indoor mockup scenarios executing two different tasks. In the first one, the aerial robot is equipped with a hook for retrieving a parcel from a shelf, whereas in the second scenario a parcel is delivered through the window of a user's home.

Autonomous UAV inspection in industrial refineries demands precise landing on pipelines under GNSS-denied conditions. This work presents a complete software architecture that integrates LiDAR-based localization with a novel multi-sensor framework for pipeline perception and tracking. The proposed method processes dual 2D laser scans and fuses them with depth data through a temporal filter. This enables consistent mapping and tracking of pipelines, effectively rejecting spurious detections and maintaining a reliable scene representation. The system is validated both in controlled indoor experiments and through fully autonomous landing missions in a real refinery, demonstrating robust performance and accurate alignment with the target infrastructure.

The impact between a free-floating or -flying space manipulator (SM) and an uncooperative target object is studied, for the purpose of transferring momentum from the object to the SM. The impact model employed to simulate contact between the robot end-effector and the object is discrete and impulsive, and it accounts for energy dissipation during impact by using the well-known Newton’s hypothesis and energetic’s definition of the coefficient of restitution (CoR). The resulting CoR is implemented to calculate the impact force, post-impact velocity of the object, and generalized rates of the SM after the collision. The motion of the SM is controlled before and after the collision via a nonlinear optimal approach, the so-called state-dependent Riccati equation (SDRE). The SDRE is a state-feedback controller design; however, a recent modification presented a design to apply the SDRE to the output feedback control. The output- and state-dependent Riccati equation (OSDRE) uses a transformation between the output variables and the states to bypass the kinematics of a system and control the outputs directly. This led to point-to-point motion control of SMs in free-floating mode using the base to push the end-effector towards the desired position. A combination of the OSDRE and the aforementioned impact modeling is introduced in this work to transfer the energy and momentum of a non-cooperative object in space to an SM, thus achieving a nearly stationary condition of the object, amenable to subsequent capture. The specific application directs attention to the collection of uncooperative space debris, which is a prominent topic in space robotics. A planar system case study is presented to discuss and demonstrate the application in various scenarios.

This tutorial paper explains how to develop a ROS2 Gazebo simulator of a dual arm aerial manipulation robot intended to conduct parcel delivery operations in a representative intra-logistics scenario. This work follows a real-to-sim approach in the sense that the simulator replicates the aerial robot, scenario, and operation carried out by a fullyautonomous dual arm aerial delivery robot [1] during the euROBIN Project Cooperative Competition. The aerial robot consists of a quad-rotor platform controlled with the PX4 flight control software, equipped with a lightweight and human-size dual arm manipulator providing two joints per arm (shoulder and elbow pitch flexion/extension), integrating a camera for parcel detection and localization with Aruco markers, and a 2D LiDAR for localization and mapping. The paper outlines the process of creating the ROS2 simulator package from the 3D model and physical parameters of the robot and the objects in the scenario relying on the examples from PX4, including a 3D mesh of the flying arena scanned from the real scenario. The purpose of this work is to serve as guideline for students and young researchers in the development of an aerial robot simulator.

This work presents a lightweight, high-level planner for UAV navigation based on a Soft Actor-Critic (SAC) reinforcement learning policy. Trained in a simplified 3D simulation, the policy generates velocity commands to reach static goals and generalizes zero-shot to dynamic tasks, including trajectory tracking and pursuit of moving targets. A parallelized PyTorch implementation accelerates training, enabling convergence in under five minutes on accessible computing hardware. The policy was validated in SITL and controlled indoor flight experiments using a real UAV with Vicon-based localization. Results demonstrate that a policy trained under simplified assumptions can generalize to multiple navigation-related tasks while requiring modest onboard computational resources. A video demonstration of the main experiments can be found at https://youtu.be/R2PkxlgmO74.

Aerial manipulators are advancing beyond traditional inspection roles to enable complex interactions with flexible structures. Applications such as structural health monitoring, and especially agricultural tasks like fruit harvesting or environmental monitoring, require inducing controlled vibrations into flexible elements. However, current solutions for controlled shaking of trees with aerial manipulators are limited to push and pull forces applied through translational movements, without exploiting the fully-capabilities of aerial platforms. This paper introduces a controlled shaking strategy that enables interaction with trees using both linear movements generated by forces (translation strategy) and rotational movements generated by torques (rotation strategy) thus exploiting the different interaction capabilities of the platform. These two strategies open a previously unexplored question: which strategy is more effective given a specific interaction point? To address this, the two interaction strategies are integrated with the Rayligh-Ritz model of the tree, obtaining the closed-loop dynamics of the system during the vibration. These closed-loop dynamics are then analyzed for the two shaking strategies, deriving which one is better for achieving higher oscillation amplitudes or frequencies. This analysis shows that, for a given interaction point of the tree trunk, this decision depends only on the platform's physical characteristics, such as mass and inertia. Finally, the theoretical analysis is experimentally validated with a hand-made bamboo tree and a fully-actuated platform through indoors flights.

Event-based vision has recently emerged as a powerful paradigm for capturing visual information with high temporal precision and minimal redundancy. Unlike traditional frame-based cameras, event cameras asynchronously detect per-pixel brightness changes with microsecond resolution. This bio-inspired sensing approach offers substantial advantages for dynamic scenes, low-latency perception, and energy-efficient computation, making it highly relevant to applications in robotics, vision, and neuromorphic computing. A fundamental tool in analyzing visual and temporal signals is the Fast Fourier Transform (FFT), which efficiently computes the spectral representation and forms the foundation of modern signal processing, communication, and control systems. However, extending Fourier analysis to asynchronous, event-driven data poses unique computational and theoretical challenges. This paper presents an open-source implementation of the event-based Fourier Transform (eFFT), a novel algorithm for efficiently computing the exact 2D discrete Fourier transform of the spatial information in asynchronous events generated by an event camera. The proposed eFFT-C++ translates the theoretical method into a modular and optimized C++17 library, implemented as a header-only package with Eigen3 dependencies. It supports both event-by-event and packet-based processing modes, reusing intermediate computations to minimize overhead while allowing the current 2D spectrum to be queried after each update. Benchmarks against FFTW3 demonstrate exactness and efficiency, with per-event update times in the order of microsecond for common frame sizes. The library enables reproducible research, provides validated code to accompany the original eFFT publication, and offers a foundation for extending event-based frequency analysis to robotics, computer vision, and neuromorphic computing in applications where rapidly updating spectra is useful, such as denoising and filtering, pattern analysis, or tracking.

This paper presents the design, development, and validation of a fully autonomous dual-arm aerial robot capable of mapping, localizing, planning, and grasping parcels in an intra-logistics scenario. The aerial robot is intended to operate in a scenario comprising several supply points, delivery points, parcels with tags, and obstacles, generating the mission plan from the voice commands given by the user. The paper derives a transferability model of the scenario, the robot, and the task, so that the proposed system design can be generalized to different scenarios (environment transfer) and platforms (embodiment transfer). The proposed transferable system architecture allows the integration of software modules managed by the Aerial Delivery Robot Operations Manager (ADROM) through the Module Interface Instances (MII) that handle the requests and the signals involved during the execution of the operation. The performance of the developed system was evaluated as part of the euROBIN Nancy Competition, conducting more than 50 flight tests. The software modules are open source, making the flight dataset also publicly available.

Doctoral Networks (DNs) aim to address systemic challenges in doctoral education, such as fostering interdisciplinarity, enabling international and intersectoral collaboration, enhancing employability, and promoting responsible innovation. While cohort-based training helps mitigate student isolation through workshops and summer schools, traditional DNs often struggle to fully realise their collaborative potential, often relying on predefined supervisor relationships or the initiative of individual researchers. In contrast, Marie Skłodowska-Curie Doctoral Networks (MSCA-DNs) prioritise doctoral candidates (DCs), challenging them to balance independent research with contributions to a shared, mission-driven objective. This study examines how structured training, including digital communities and application-focused research sprints, enhances system integration and collaboration within the Robotics and AI for Critical Asset Monitoring (RAICAM) Doctoral Network. DCs located across seven European countries worked in virtual teams, refining systems through structured workflows, weekly meetings, and shared workspaces before training schools. Through continuous online collaboration and targeted sprints, RAICAM facilitated interdisciplinary integration. Two research sprints, conducted in Italy and France, allowed teams to develop and test solutions for real-world challenges with an impact-driven plan that considers a given problem from and end-to-end perspective that requires and foster interdisciplinary collaboration. The results highlight the effectiveness of structured training in enhancing collaboration and adaptability, while identifying key areas for improvement. This study translates lessons from RAICAM into practical guidelines for future doctoral networks, demonstrating how structured training empowers students to drive interdisciplinary research independently.

This paper aims to introduce a standardized test methodology for drone detection, tracking, and identification systems. It is the aim that this standardized test methodology for assessing the performance of counter-drone systems will lead to a much better understanding of the capabilities of these solutions. This is urgently needed, as there is an increase in drone threats and there are no cohesive policies to evaluate the performance of these systems and hence mitigate and manage the threat. The presented methodology has been developed within the framework of the project COURAGEOUS funded by European Union’s Internal Security Fund Police. This standardized test methodology is based upon a series of standard user-defined scenarios representing a wide set of use cases. At this moment, these standard scenarios are geared toward civil security end users. However, the proposed standard methodology provides an open architecture where the standard scenarios can be modularly extended, providing standard users the possibility to easily add new scenarios. For each of these scenarios, operational needs and functional performance requirements are provided. Using this information, an integral test methodology is presented that allows for a fair qualitative and quantitative comparison between different counter-drone systems. The standard test methodology concentrates on the qualitative and quantitative evaluation of counter-drone systems. This test methodology was validated during three user-scripted validation trials.

Bridges are vital infrastructure assets whose maintenance is essential to ensure safety and efficient traffic flow. However, due to their nature, they are often located in hard-toreach places, which makes their regular inspection challenging and risky for human operators. This paper presents a comprehensive multi-UAV (Unmanned Aerial Vehicle) framework for fast and efficient cooperative bridge inspection, leveraging commercial UAVs with open-source tools and integrating both a custom Ground Control Station and advanced multi-UAV motion planning based on Signal Temporal Logic. The proposed approach enables autonomous and safe data collection while minimizing operational constraints and human intervention, making it a valuable contribution to UAV-based infrastructure monitoring. The presented framework has been validated in a real-world environment, showcasing its effectiveness in coordinating UAV teams for autonomous structural inspections.

Unmanned aerial vehicles (UAVs) operating in uncertain environments must plan safe and efficient trajectories while avoiding obstacles. This work addresses this challenge by formulating UAV path planning as a stochastic optimal control problem using covariance control. The objective is to generate a closed-loop guidance policy that steers both the mean and covariance of the UAV’s state toward a desired target distribution while ensuring probabilistic collision avoidance with ellipsoidal obstacles. The stochastic problem is convexified and reformulated as a sequence of deterministic optimization problems, enabling efficient computation even from coarse initial guesses. Simulation results demonstrate that the proposed method successfully produces robust trajectories and feedback policies that satisfy chance constraints on obstacle avoidance and reach the target with prescribed statistical characteristics.