Paulo José Costa

Abstract

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

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
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.