Abstract
Robotics competitions are a way to challenge researchers, roboticists and enthusiastic to address robot applications. One of the well-known international competition is the Micromouse where the fastest mobile robot to solve a maze is the winner. There are several topics addressed in this competition such as robot prototyping, control, electronics, path planning, optimization, among others while keeping the size of the robot as small as possible. A simulation can be used to speed-up the development and testing algorithms but faces the gap between a simulation and reality, specially in the dynamics behaviour. There are some simulation environments that allow to simulate the Micromouse competition, but in this paper, an Hardware-in-the-loop simulator tool is presented where the simulated robot is controlled by the same microcontroller used by the robot. By this way, the developed algorithms are tested and validated with the limitations and constraints presented in the real hardware, such as memory and processing capabilities. The robot dynamics, the slippage of the wheels, the friction and the 3D visualization are present in the simulator. The presented results show that the same code and hardware controlling the simulated and the real robot identically.

Abstract
Path planning for mobile robotics in unknown environments or with moving obstacles requires re-planning paths based on information gathered from the surroundings. Moving obstacles and real time constraints require fast computing to navigate and make decisions in a mobile robot. This paper addresses an optimization approach to compute, with real time constraints, the optimal path for a mobile robot based on a dynamically simplified A* search algorithm with a commitment on the available time.

Abstract
Inertial measurement units (IMU) are, typically, a cluster of accelerometers, gyroscopes and magnetometers. Its use was introduced with military applications, being, nowadays, widely common on industrial applications, namely robot navigation. Since there are a lot of units in different cost ranges, it is proposed, in this paper, a procedure to compare their performance in tracking tasks. Once IMU samples are unavoidably corrupted by systematic and stochastic errors, a calibration procedure (without any external equipment) to identify sensors’ error models and a Kalman filter implementation to remove white noise are suggested. Then, the comparison is carried out over two trajectories, square and circular paths, respectively, being described by a robotic arm, which acts as reference. The results show that different manufacturing quality units can track, with success, orientation references but are incapable to perform position tracking activities. © 2019, Springer Nature Switzerland AG.

Abstract
Mobile robotic applications are increasing in several areas not only in industries but also service robots. The Industry 4.0 promoted even more the digitalization of factories that opened space for smart-factories implementation. Robotic competitions are a key to improve research and to motivate learning. This paper addresses a new competition proposal, the Robot@Factory Lite, in the scope of the Portuguese Robotics Open. Beyond the competition, a reference robot with all its components is proposed and a simulation environment is also provided. To minimize the gap between the simulation and the real implementation, an Hardware-in-the-loop technique is proposed that allows to control the simulation with a real Arduino board. Results show the same code, and hardware, can control both simulation model and real robot.

Abstract
Mobile robot applications are increasing its usability in industries and services (examples: vacuum cleaning, painting and farming robots, among others). Some of the applications require that the robot moves in an environment between two positions while others require that the robot scans all the positions (Coverage Path Planning). Optimizing the traveled distance or the time to scan the path, should be done in order to reduce the costs. This paper addresses an optimization approach of the coverage path planning using Q-Learning algorithm. Comparison with other methods allows to validate the methodology.

Abstract
The coordination problem in multi-AGV systems can be approached as an optimization problem and aims to make possible the execution of several tasks simultaneously, avoiding collision and deadlock situations and reducing the average execution time. Time Enhanced A* (TEA*) is one of the path planning algorithms developed for this purpose. This paper focus on the implementation of the TEA* for real industrial applications. In that context, a new approach was developed to complement the TEA* with the capacity to approximate the planning of the future positions for differential robots with its real behaviour.