Event-based cameras generate asynchronous streams of events, triggered proportionally to the logarithmic change of brightness in the scene. These cameras have very low latency and high dynamic range suitable to address challenging motion scenarios in robotics. In this work, we explore a new event-based line-SLAM approach following a parallel tracking and mapping philosophy. Our fast tracking algorithm, produces accurate camera pose estimates at a high rate by minimizing the event-line reprojection error with an error-state Kalman filter formulated entirely with Lie theory. The mapping thread leverages the natural edge highlighting strength of events to recover and optimize straight lines in human-made scenarios. The proper manipulation of matrix sparsity as well as the information sharing between tracking and mapping nodes allow us to achieve real-time performance on a standard multi-core CPU. This system was tested on several scenarios rich in straight edge objects, and compared against, ground truth and frame and event based state-of-the-art approaches.

In today’s world, the integration of renewable energies is essential to meet the surging global energy demand and reduce pollution. Ecuador, with its favorable geographical location, boasts abundant renewable resources. Currently, the country has large-scale renewable energy projects such as “El Aromo” in Manabí, led by Solarpack, and Villonaco in Loja, with its wind power plant. In this study, Homer Pro software is used to simulate two microgrids with solar and wind energy in the mentioned sectors, allowing us to conduct comprehensive economic and energy analyses to determine the most viable configurations.

This article presents a 2D pose estimation method for an omnidirectional mobile robot with Mecanum wheels, using an extended Kalman filter (EKF) formulated on the Lie group $\mathrm{SO}\left(2\right)$. The purpose is estimate the robot’s position and orientation by fusing angular velocity measurements from the wheel encoders with data from an IMU. Employing Lie algebra, the EKF provides a consistent and compact representation of rotational motion, improving prediction and update steps. The filter was implemented in ROS 1 and validated in simulation using Gazebo, with a reference trajectory and real measurements used for evaluation. The system delivers higher pose estimation precision, validating the effectiveness in rotational maneuvers.

Lithium-ion batteries have become a beacon in modern energy storage, powering from small electronic devices to electric vehicles (EVs) and critical medical equipment. Since their commercial introduction in the 1990s, significant advancements in materials science and engineering have enhanced battery capacity, safety, and lifespan. However, the complexity of lithium-ion battery dynamics has necessitated the development of advanced charging and control strategies to optimize performance, safety, and longevity. This work proposes a comparative analysis of three advanced control methods for lithium-ion battery charging: reinforcement learning, fuzzy logic, and classic proportional–integral–derivative (PID) control. Traditional charging methods often fail to address the complexities of battery dynamics, leading to suboptimal performance. Our study evaluates these intelligent control strategies using MATLAB-Simulink simulations to enhance charging efficiency, speed, and battery lifespan. The findings indicate that reinforcement learning offers superior adaptability, fuzzy logic provides robust handling of nonlinearity, and PID control ensures reliable performance with minimal computational resources.