Abstract
This study focuses on the role of autonomous control systems in robotics, focusing on how robot controls the actuator movements after meticulous information processing and decision-making within the robotic framework ROS. To go on this experimental challenge, a diesel tractor was modified into a versatile experimental platform capable of autonomous navigation and control. At the center of this tractor is the sensory module term1ed "Sentry," which consists of a network of interconnected sensors that have been methodically integrated to enable comprehensive ambient perception. The sensors use advanced technologies like 3D 360º LiDAR for spatial mapping, thermal cameras for object detection, RGBD cameras for visual perception, a microcontroller for control, GPS+RTK for precise positioning and a Jetson Xavier for high-performance computing. The experimental assessments done in this work covered a wide range of scenarios, from simulated environments with controlled variables to real-world terrains rife with uncertainty and variability. Valuable insights were gained by analyzing the resulting data, revealing light on the system's operation, performance, and efficacy under various scenarios. © 2024, International Frequency Sensor Association (IFSA). All rights reserved.

Abstract
The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Abstract
The advancement of technology has led to a growing demand for autonomy across various sectors. A key aspect of achieving autonomous navigation through intricate environments is path planning, initially confined to 2D spaces but rapidly evolving to address the complexities of 3D environments. Despite the widespread adoption of RRT-based planners, their inherent lack of optimality has encouraged researchers to find refinements. This paper transposes an existing algorithm developed for 2D environments to 3D, leveraging a heuristic to optimize the generated paths in terms of path length, memory consumed, and execution time. Along with this scalability to 3D scenarios, a modification was introduced that trades off some execution time for a substantial improvement in path length. The results obtained from a series of simulated experimental tests prove the efficacy of the proposed method in 3D environments, demonstrating reduced memory consumption and execution time compared to conventional approaches.

Abstract
The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

Abstract
This paper discusses the emerging field of robotics, particularly focusing on motion planning for robotic manipulators. It highlights the need for simplification and standardization in robot implementation processes. Among several tools available, the paper focuses on the MoveIt tool due to its compatibility, popularity, and community contributions. However, the paper acknowledges some resistance in developing new applications with MoveIt, especially for researchers and beginners. To address this, the paper introduces an efficient, modular action server for interacting with the MoveIt framework. This pipeline simplifies parameter reconfiguration and provides a general solution for the motion planning problem. It can calculate trajectories for robotic manipulators without environmental collisions using a single server request and supports operation in different modes. The server was tested on an Universal Robots UR10 manipulator, demonstrating its ability to quickly plan paths for two test operations: an object pick-and-place mission and a collision avoidance test. The results were positive, achieving the set goals with minimal user-server interaction. This work represents a significant step towards more efficient and user-friendly robotic manipulation.

Abstract