Maritime operations, such as ship hull inspections, infrastructure assessments, and rapid response to emergencies, often expose humans to hazardous environments. The deployment of unmanned vehicles offers a potential solution to mitigate risks and reduce operational costs in such scenarios. Collaborative efforts between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) can enhance mission endurance and portability. This paper introduces a cooperative UAV-USV system designed to expand the operational coverage area. It leverages off-the-shelf satellite-based technology to establish robust communication links between UAVs, USVs, and the ground station. This connection facilitates the development of an autonomous launch and recovery system, relying on GPS and visual servoing. Furthermore, the system incorporates a Battery Hot Swap-system (BHS) on the USV, allowing the UAV to resume its mission within 3 minutes. The effectiveness of this proposed solution was demonstrated through successful inspection trials conducted at the port of Hamburg, Germany.

This study presents a real-time, adaptive UAV system designed to enhance ecological surveys by overcoming the trade-off between wide-area coverage and high-resolution data collection. The Modular Detection and Targeting System (MDTS) integrates thermal imaging for broad detection and high-resolution RGB zoom imaging for precise species identification. Field trials demonstrated the system’s ability to detect and record both avian and mammalian species with significantly reduced redundant data and improved survey efficiency. Compared to traditional UAV methods, the MDTS achieved over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume. The system’s modular design enables rapid adaptation to diverse ecological applications, providing classification-ready data while minimizing post-processing demands. These results highlight the MDTS as a scalable, efficient tool for wildlife monitoring and environmental research, bridging the gap between detection and actionable ecological insights.

With the development of Autonomous Unmanned Aerial Vehicle's (UAV's), Precise state estimation is a fundamental aspect of autonomous flight and plays a critical role in enabling robots specially in GPS denied environment to operate safely, reliably, and effectively across a wide range of applications and operational scenarios. In this paper, we propose a tightly-coupled multi-sensor filtering framework for robust UAV/UGV state estimation, which integrates data from an Inertial Measurement Unit (IMU), a stereo camera, GPS, and 3D range measurements from two Light Detection and Ranging (LiDAR) sensors. The proposed LGVINS system significantly improves the accuracy and robustness of state estimation in both structured and unstructured outdoor environments, such as bridge inspections, open fields, urban city and areas near buildings. It also improves positioning accuracy in scenarios with or without GPS signals. The goal is to exploit the fact that these sensor modalities have mutually exclusive strengths, the visual, inertial and the Lidar sensor techniques are implemented to compensate for the robots state estimate errors in multiple outdoor challenging environment. It effectively reduces long-term trajectory drift and ensures smooth, continuous state estimation, regardless of GPS satellite availability. We demonstrate and evaluate the LGVINS approach on public dataset as well as our own dataset collected from the proposed hardware integration on UAV, deployed on computationally-constrained systems. This demonstrates that the proposed system achieves higher accuracy and robustness in state estimation across various environments compared to currently available methods.

This paper develops on the process of inverse kinematics (IK) for manipulator robots based on a recent technique from the computer graphics area that has been highlighted by its simplicity and low computational cost. This IK solver is known as Forward And Backward Reaching Inverse Kinematics (FABRIK) suffers from singularities when applied in kinematic chains composed of one degree of freedom (1-DOF) joints. Some extensions of this method have been proposed to incorporate the constraints in some of the most common joints, however, their application over kinematic chains with only 1-DOF joints is still not possible. Since several manipulators have kinematic chains composed of 1-DOF joints, this work presents a new method, named FABRIK-R, to extend the original method for applications in the robotics manipulators field.

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning.

The evolution of manipulator robots has increased the complexity of their models and applications, requiring that the inverse kinematics (IK) methods integrated into their control systems to have features such as fast convergence, completeness, low computational cost, and the ability to avoid local minima and singularities. We propose in this paper a new probabilistic IK solver based on the probabilistic search method known as Rapidly-Exploring Random Tree (RRT), the Workspace-RRT. The technique grows the tree as a spatial representation of the manipulator on the workspace instead of the configuration space, which reduces the search space up to 3 dimensions. Based on this new representation we also present the Manipulator-Based Rapidly Random Tree (MB-RRT) by incorporating to the Workspace-RRT a new probability model and a new metric for the closest node. We evaluate the presented methods through simulated experiments in the Matlab software. First, we evaluate the impact of the proposed aspects through a comparison between the RRT-based IK solvers, which emphasizes the proposed changes as a key to make the method suitable for the IK problem. At last, we show the use of the MB-RRT for precision tasks and obstructed environments.