Modelling of extended de-weight fuzzy control for an upper-limb exoskeleton

Performing heavy physical tasks, overhead work and long working hours are some examples of activities that can lead to musculoskeletal problems in humans. To overcome this issue, automated robots such as the upper-limb exoskeleton is used to assist humans while performing tasks. However, several concerns in developing the exoskeleton have been raised such as the control strategies used. In this study, a control strategy known as the extended de-weight fuzz was proposed to ensure that the exoskeleton could be maneuvered to the desired position with the least number of errors and minimum torque requirement. The extended de-weight fuzzy is a combination of the fuzzy-based PD and fuzzy-based de-weight controller systems. The extended de-weight fuzzy was then compared with the fuzzy-based PD and PID controllers, and the performances of these controllers were compared in terms of their deviations and required torques to perform tasks. The findings show that the proposed control strategy performs better than the fuzzy-based PD and PID controller systems

Implementation of Mobile Robot’s Navigation using SLAM based on Cloud Computing

This paper is concerned with the implementation of EKF-SLAM (Extended Kalman Filter- Simultaneous Localization and Mapping) algorithm using a cloud computing architecture based on ROS (Robot Operating System). The localization and mapping is essential step to navigate a mobile robot in unknown environment. The implemented EKF-SLAM has used a landmark that sensed using IR Emitter sensor provided by the Kinect camera to update a map of the environment and simultaneously estimate the robot’s position and orientation within the map. The implementation was done using three parts. The first one was the TurleBot Mobile robot with the Kinect camera, which was simulated in Gazebo environment. The second part was the EKF-SLAM running under the MATLAB to generate the Map and Location data. The third part was the ROS Master node, which runs on the cloud to enable part one and two to communicate using topics. The scan data from Kinect camera and the location data from the odometer is transferred from the first part to the second part through ROS Master node after impaired with zero mean Gaussian noise . Then the second part performs EKF-SLAM and transmit the corrections to the first part through the ROS Master node as well

Entorno virtual para el estudio de señales EMG

En este proyecto se amplía el sistema de simulación en Unity3D realizado para la mano robótica Allegro a la mano Shadow. Además se añade la posibilidad de poder cargar las manos con textura realista. Permite el control de la mano Shadow de manera remota mediante el nodo de ROS correspondiente. Se integra también con un módulo de adquisición de señales que pasa la información a los nodos usando ROS mediante Matlab. Para el control de la simulación mediante señales EMG, se definen unos comandos de control en el entorno de simulación que pueden ser utilizados desde dichas señales o por el usuario desde la interfaz. El sistema posibilita el control de dos manos de manera simultánea y en el caso de la mano Allegro con un control colaborativo. Como resultado se ha obtenido un sistema intuitivo. Con respecto a la conexión con las manos, el sistema tiene una buena tasa de actualización en los sistemas Allegro. En el caso de la Shadow el retardo se incrementa debido al uso del planificador, siendo mayor este retardo el sistema simulado que en el real