A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface ElectromyographyThis paper presents a system that combines computer vision and surface electromyography techniques to perform grasping tasks with a robotic hand. In order to achieve a reliable grasping action, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. Nowadays, robots can perform a variety of tasks to help human operators in their work [ 1 ]. The use of robots to collaborate with people with disabilities in industrial environments is a growing sector. For instance, several studies analyse the execution of manufacturing tasks by disabled people [ 2 , 3 ].
A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography
Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering , it seeks to automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring , processing , analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.
Machine vision is a major discipline derived from artificial intelligence research. - To browse Academia.
Machine vision MV is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control , and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision , a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environments such as security and vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution.
I've spent 3 months at Instituto Superior Tecnico in Lisbon working with dr. Lourdes Agapito. Download CV. My current research interests are in the application of machine learning and artificial intelligence techniques for industrial applications, with particular attention to the emerging fields of collaborative robotics and reinforcement learning for situation awareness decision making. You can find the full list of my publications here , but the best way is to see my Google Scholar profile. Journal Papers Count on me: learning to count on a single image Setti F. Computer Vision and Image Understanding, Volume , pp.