This paper presents the integration of multimodal sensor systems for an autonomous forestry machine. The utilized technology is housed in a single enclosure which consolidates a set of components responsible for executing machine control actions and comprehending its behavior in various scenarios. This sensor box, named Sentry, will subsequently be connected to a forestry machine from MDB, model LV600 PRO. The article outlines previous work in this field and then details the integration and operation of the equipment, integrated into the forest machine, providing descriptions of the adopted architecture at both the hardware and software levels. The gathered data enables the assessment of the forestry machine’s orientation and position based on the information collected by the sensors. Finally, practical experiments are presented to demonstrate the system’s behavior and to analyze the methods to be employed for autonomous navigation, thereby assessing the performance of the established architecture. The novel aspects of this work include the physical and digital integration of a multimodal sensor system on a forestry machine, its use in a real case scenario, namely, forest vegetation removal, and the strategies adopted to improve the machine localization and navigation performance on unstructured environments.

Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system’s robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

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).

The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV’s navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV’s position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper.

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.

Swarm robotics (SR) and swarm intelligence (SI) share decentralization and collective behavior but address different problems. SR concerns physical multi-robot systems whose collective behavior must hold under sensing, communication, energy, and safety constraints, while SI includes computational models and optimization methods, many of which involve no robots. Conflating them obscures which SI results transfer to robot swarms and what machine learning (ML) contributes. This review examines SR coordination, SI optimization, and ML-augmented hybrid methods from a deployment perspective. It proposes a tiered framework for deciding whether a system is a robotic swarm, a taxonomy of five hybrid SR–SI–ML architecture types, and feasibility conditions for combining methods on a given platform. Across 17 coded studies, SI optimizers such as Ant Colony Optimization and Particle Swarm Optimization are evaluated mainly on benchmarks or in simulation and reach hardware only with supporting infrastructure; hardware experiments, with one exception, use twenty robots or fewer, and learned controllers transfer to hardware in some studies but show a large simulation-to-reality gap in others. Layered hybrid architectures, in which SR provides coordination, SI provides optimization and ML provides adaptation, remain a design hypothesis. The review proposes a reporting checklist for future studies.