Assistant robot through deep learning

Abstract

This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.

Three-Dimensional Unified Motion Control of a Robotic Standing Wheelchair for Rehabilitation Purposes

“…Within this group, we find robot pets that stimulate the development of children, mobile manipulators that transport heavy loads, and assistance robots that mobilize or perform rehabilitation procedures on people with health problems, among others [ 2 , 3 ]. According to their applications, robots can be subdivided into six main groups: ( i) Military robots , which are used to perform tasks of exploration, surveillance, security, rescue, etc., and are responsible for carrying out activities in hostile environments that can be considered high-risk for people [ 4 , 5 ]; ( ii) Construction robots have the ability to be programmed to perform complex tasks or operate semi-autonomously, aimed at improving productivity as well as accident prevention in the workplace [ 6 , 7 ]; ( iii) Field robots have transformed various aspects of the agricultural, livestock, and mining industries, reducing production costs and increasing profits, thus increasing the economy of population [ 8 , 9 ]; (iv) Learning robots, which are didactic tools that improve the learning process, and they are regularly implemented in stimulation therapies for children with learning problems such as autism and attention deficit, among others [ 2 , 10 , 11 ]; ( v) Entertainment robots , capable of performing various tasks with the aim of entertaining and accompanying human beings, they are commonly used as interactive toys for children [ 12 ]; and finally ( vi) Robots in medicine, which can be autonomous or tele-operated. They are classified into surgeon robots, prosthetic robots, therapeutic robots, and assistance robots, considered as precise mechanisms with high accuracy due to the large number of sensors and actuators that have this technology [ 13 , 14 ].…”

Robotic hex-nut sorting system with deep learning

“…Figure 1 (left) illustrates the test environment used, where the black background where the nuts to be relocated would be found is highlighted. The system has an interface that allows human intervention and control, through the Telegram application [29] capable of operating from an Android or IOS cell phone. Figure 1 (right) illustrates the local controller based on a raspberry pi 2 model B, programed in python, the pi-camera V1 of the raspberry was used for image acquisition, and its GPIO module is used as a driver to each servomotor.…”

Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems

“…In [13], we are talking about assistive robotics and possibility of using three convolutional neural networks that are trained, one for recognizing group of tools, second for speech recognition, trained with names of tools, and third for recognition of user's hand, taking in consideration classification of 2 gestures: open and closed hand. As a result, authors have developed functional application for assistive robotics capable of receiving and correctly identifying voice commands, associating them with physical object, picking it up and delivering it to user's hands.…”