Designing Decentralized controllers for distributed-air-jet MEMS-based micromanipulators by reinforcement learning.

International audienceDistributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don't produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object. Reinforcement learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. We show how to apply reinforcement learning in a decentralized perspective and in order to address the global-local trade-off. The simulation results demonstrate that the reinforcement learning method is a promising way to design control laws for such distributed systems

Development of a Wearable Exoskeleton for Arm Rehabilitation

With the increasing population of aging and disabled individuals, the need for a more effective and efficient solutions is at peak, Powered Exoskeletons are wearable robots that can be attached to the disabled limb with the goal of adding power to, or rectifying the limb functionality , one of its application is rehabilitation. This study review relevant research, technologies and products, while critically analyzing them and addressing some of the current problem faced by the researchers in this field, such as the use EMG signal as a primary input to the controller. This research propose an adaptive EMG-based upper limb exoskeleton that is built on a fuzzy controller. The paper strives to propose a wearable general-user Exoskeleton, Built around an interactive gaming interface to engage the patients in the rehabilitation process. The games and exoskeleton assistance degree can be preset – on medical supervision – to different training patterns. Ultimately, the project strives to afford normal daily life for those who needs it

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion