Abstract
This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

Abstract
In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

Abstract

Abstract
The management of health systems has been one of the main challenges in several European countries, especially where the aging population is increasing. This led to the adoption of smarter technologies as a means to automate the processes within hospitals. One of the technologies adopted is active location solutions, which allows the staff within the hospital to quickly find any sort of entity, from key persons to equipment. In this work, we focus on developing a reliable method for active location based on RSSI antennas, passive tags, and ML models. Since the tags are passive, the usage of RSSI is discouraged, since it does not vary sufficiently based on our experiments. We explored the usage of alternative features, such as the number of activations per tag within a time slot. Throughout our evaluation, we were able to reach an average error of 0.275 m which is similar to existing RSSI IPS.

Abstract
The development of technologies to enable robots to operate autonomously in challenging forest environments is crucial for promoting effective natural resource management and preventing forest fires, standing out as a priority on environmental conservation and public safety agendas. This article presents a detailed discussion on the development of an innovative sensory architecture, specifically designed to integrate a wide range of advanced sensors. The main objective of this architecture is to provide highly accurate inputs to a system, thereby empowering a forest robot to make autonomous and adaptive decisions in real-time. To achieve this ambitious goal, the proposed sensory architecture defines a comprehensive set of crucial variables, which are carefully selected and strategically integrated. This design results in a distributed system capable of processing multiple subsystems in parallel and efficiently. This innovative approach enables the conversion of a conventional forest mulcher machine into a fully autonomous and highly intelligent forest robot. Furthermore, the article details the procedures and methodologies used to experimentally validate the robustness and effectiveness of the developed system. Through rigorous testing and comprehensive analyses, the system's ability to handle a variety of adverse environmental conditions and typical operational challenges in forest environments is demonstrated. These experimental validations are essential to ensure the reliability and accuracy of the system in real-world situations. © 2024, International Frequency Sensor Association (IFSA). All rights reserved.

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.