Abstract
Several approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. The current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian Optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and theta) of the robot were optimized in four different scenarios of the same context. The results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.

Abstract
Mecanum wheeled mobile robots have become relevant due to their excellent maneuverability, enabling omnidirectional motion in constrained environments as a requirement in industrial automation, logistics, and service robotics. This paper addresses a low-level controller based on the H-Infinity (H-infinity) control method for a four-wheel Mecanum mobile robot. The proposed controller ensures stability and performance despite model uncertainties and external disturbances. The dynamic model of the robot was developed and introduced in MATLAB to generate the controller. Further, the controller's performance is validated and compared to a traditional PID controller using the SimTwo simulator, a realistic physics-based simulator with dynamics of rigid bodies incorporating non-linearities such as motor dynamics and friction effects. The preliminary simulation results show that the H-infinity reached a time-independent Euclidean error of 0.0091 m, compared to 0.0154 m error for the PID in trajectory tracking. Demonstrating that the H-infinity controller handles nonlinear dynamics and disturbances, ensuring precise trajectory tracking and improved system performance. This research validates the proposed approach for advanced control of Mecanum wheeled robots.

Abstract
This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.

Abstract
Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning solution employing Convolutional Neural Networks (CNN) to tackle the localization problem, specifically in the context of the RobotAtFactory 4.0 competition. The proposed approach leverages transfer learning from the pre-trained VGG16 model to capitalize on its existing knowledge. To validate the effectiveness of the approach, a simulated scenario was employed. The experimental results demonstrated an error within the millimeter scale and rapid response times in milliseconds. Notably, the presented approach offers several advantages, including a consistent model size regardless of the number of training images utilized and the elimination of the need to know the absolute positions of the fiducial markers.

Abstract
This article presents the development of a low-cost 3D mapping technology for trajectory planning using a 2D LiDAR and a stepper motor. The research covers the design and implementation of a circuit board to connect and control all components, including the LiDAR and motor. In addition, a 3D printed support structure was developed to connect the LiDAR to the motor shaft. System data acquisition and processing are addressed, as well as the generation of the point cloud and the application of the A* algorithm for trajectory planning. Experimental results demonstrate the effectiveness and feasibility of the proposed technology for low-cost 3D mapping and trajectory planning applications.

Abstract
Artificial Intelligence has been introduced in many applications, namely in artificial vision-based systems with object detection tasks. This paper presents an object localization system with a motivation to use it in autonomous mobile robots at robotics competitions. The system aims to allow robots to accomplish their tasks more efficiently. Object detection is performed using a camera and artificial intelligence based on the YOLOv4 Tiny detection model. An algorithm was developed that uses the data from the system to estimate the parameters of location, distance, and orientation based on the pinhole camera model and trigonometric modelling. It can be used in smart identification procedures of objects. Practical tests and results are presented, constantly locating the objects and with errors between 0.16 and 3.8 cm, concluding that the object localization system is adequate for autonomous mobile robots. © 2023 IEEE.