Visually impaired and blind people (VIBP) have to face significant difficulties locating public transport vehicles and bus stops due to their vision restrictions. Over the past decade, diverse assistance systems have been developed to solve this problem. However, most of them are based upon the global positioning system (GPS) and present satellite coverage problems in indoor environments. Some others are wearable prototypes that turn out to be onerous for the user. This paper presents an assistance system for VIBP in the use of public transportation. The proposed system uses Bluetooth Low Energy (BLE) technology for location and communication purposes, and a mobile application for user-smartphone interaction. The BLE beacons are installed on buses and their stops; accordingly, the mobile application tracks them in real-time and provides the relevant information to the user employing verbal instructions; transportation line, destination, next stop name, and current location. This information allows the user to properly select the desired bus in advance and get off at the correct destination stop. The proposed system has been tested in two scenarios: 1) under controlled conditions and 2) in a real environment. The results show that the proposed system is 97.6% effective when VIBP travel independently from one point to another. In addition, according to an assessment sheet completed by the participants, the proposed system grants them greater confidence and independence than GPS-based systems because of the following reasons; firstly, it can work with an internet connection or without an internet connection. Secondly, it is not an onerous system; information about the location of vehicles and stops is provided in real-time. Last but not least, it does not present satellite coverage problems in indoor environments.

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

In this paper, the modifications made to a robot Bioloid type A to participate in weightlifting competition HuroCup of the International Federation of the Association of Robot Soccer, FIRA, is presented. The changes are based on a static analysis in critical positions that the robot should take during weightlifting competition. Furthermore, the designed elements will be simulated before construction using finite element software.