Abstract
This paper focuses on the modeling, control, and simulation of an over-actuated hexacopter tilt-rotor (HTR). This configuration implies that two of the six actuators are independently tilted using servomotors, which provide high maneuverability and reliability. This approach is predicted to maintain zero pitch throughout the trajectory and is expected to improve the aircraft's steering accuracy. This arrangement is particularly beneficial for precision agriculture (PA) applications where accurate monitoring and management of crops are critical. The enhanced maneuverability allows for precise navigation in complex vineyard environments, enabling the unmanned aerial vehicle (UAV) to perform tasks such as aerial imaging and crop health monitoring. The employed control architecture consists of cascaded proportional (P)-proportional, integral and derivative (PID) controllers using the successive loop closure (SLC) method on the five controlled degrees of freedom (DoFs). Simulated results using Gazebo demonstrate that the HTR achieves stability and maneuverability throughout the flight path, significantly improving precision agriculture practices. Furthermore, a comparison of the HTR with a traditional hexacopter validates the proposed approach.

Abstract
Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.

Abstract
With the increasing adoption of mobile robots for transporting components across several locations in industries, congestion problems appear if the movement of these robots is not correctly planned. This paper introduces a fleet management system where a central agent coordinates, plans, and supervises the fleet, mitigating the risk of deadlocks and addressing issues related to delays, deviations between the planned paths and reality, and delays in communication. The system uses the TEA* graph-based path planning algorithm to plan the paths of each agent. In conjunction with the TEA* algorithm, the concepts of supervision and graph-based environment representation are introduced. The system is based on ROS framework and allows each robot to maintain its autonomy, particularly in control and localization, while aligning its path with the plan from the central agent. The effectiveness of the proposed fleet manager is demonstrated in a real scenario where robots operate on a shop floor, showing its successful implementation.

Abstract

Abstract
This article presents the development of a tethered fixed-wing tail-sitter VTOL (Vertical Take-Off and Landing) Unmanned Aerial Vehicle system. The design focuses on improving energy efficiency by utilizing the wings to harness wind power, similar to a kite, while maintaining VTOL functionality. A distinguishing feature is the purpose-built autopilot system, with custom hardware and software components specifically engineered for this application. The study presents the system identification process for obtaining five MIMO (Multiple-Input Multiple-Output) transfer functions that characterize the dynamics between roll-yaw commands and responses, including the tether angle feedback. To address the inherent coupling effects and uncertainties in the system, robust mixed sensitivity (H-infinity) MIMO controllers are developed. The controllers were validated through both simulations and experimental flights, demonstrating effective performance in handling cross-coupling effects and maintaining stability under various operating conditions. According to flight test findings, the system can precisely manage the tether angle while adjusting for ground effect disturbances. This allows for accurate tethered navigation, a stable attitude, and the maintenance of an adequate yaw heading.

Abstract
This paper presents a novel Nonlinear Model Predictive Controller (NMPC) architecture for trajectory tracking of omnidirectional robots. The key innovation lies in the method of handling constraints on maximum velocity and acceleration outside of the optimization process, significantly reducing computation time. The controller uses a simplified process model to predict the robot’s state evolution, enabling real-time cost function minimization through gradient descent methods. The cost function penalizes position and orientation errors as well as control effort variation. Experimental results compare the performance of the proposed controller with a generic Proportional-Derivative (PD) controller and a NMPC with integrated optimization constraints. The findings reveal that the proposed controller achieves higher precision than the PD controller and similar precision to the NMPC with integrated constraints, but with substantially lower computational effort. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.