Universal Robotics, Inc. is a software engineering and robotics company headquartered in Nashville, Tennessee. The company offers state-of-the-art artificial intelligence with multi-dimensional sensing and motion control to help companies automate processes, from making machines more flexible to analyzing big data.Founded in 2008, the company specializes in complex processes not previously automated. Universal’s flagship intelligence software, Neocortex, enables robots to perform tasks too costly, dangerous or impossible for humans to undertake. The technology was funded by DARPA and NASA, and was originally co-developed through a 7-year partnership between NASA and Vanderbilt University and is employed in NASA’s Robonaut.By combining the Neocortex intelligence platform with modular sensing and control software products, Universal Robotics currently provides flexible applications for materials handling. Today’s software products include 3D machine vision products . Applications include Unlimited Box Moving, Unlimited Depalletization, Random Bin Picking, Random Bag Picking, and 3D Inspection. Wikipedia.
Mallapragada G.,Pennsylvania State University |
Mallapragada G.,Universal Robotics |
Ray A.,Pennsylvania State University |
Jin X.,Pennsylvania State University
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2012
This paper presents a procedure for behavior identification of mobile robots, which requires limited or no domain knowledge of the underlying process. While the features of robot behavior are extracted by symbolic dynamic filtering of the observed time series, the behavior patterns are classified based on language measure theory. The behavior identification procedure has been experimentally validated on a networked robotic test bed by comparison with commonly used tools, namely, principal component analysis for feature extraction and Bayesian risk analysis for pattern classification. © 2012 IEEE.
Kim D.-J.,University of Central Florida |
Kim D.-J.,Universal Robotics |
Wang Z.,University of Central Florida |
Behal A.,University of Central Florida
IEEE/ASME Transactions on Mechatronics | Year: 2012
In this paper, we document the progress in the design of a motion segmentation and control strategy for a smart assistive robot arm that can provide assistance during activities of daily living to the elderly and/or users with disabilities. Interaction with the environment is made challenging by the kinematic uncertainty in the robot, imperfect sensor calibration as well as the fact that most activities of daily living are generally required to be performed in unstructured environments. The motion control strategy exploits visual and force feedback from sensors in the robots hand to provide the basis for efficient interaction with the unstructured environment. Through experimental studies with a variety of objects of daily life in natural environments, an anthropomorphic-like approach was found to be the most suitable for reliable and speedy object retrieval. Specifically, gross reaching/docking motions of the robot arm using proprioception are followed by fine alignment of the hand through visual feedback and eventually grasping based on haptic feedback. Experimental results using a wheelchair mounted robotic arm are presented to demonstrate the efficacy of the proposed algorithms. © 2012 IEEE.
Tsui K.M.,University of Massachusetts Lowell |
Tsui K.M.,Universal Robotics |
Tsui K.M.,University of Central Florida |
Behal A.,University of Central Florida |
And 2 more authors.
Applied Bionics and Biomechanics | Year: 2011
Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials. © 2011 - IOS Press and the authors. All rights reserved.
Rojas J.,Vanderbilt University |
Peters II R.A.,Universal Robotics
Autonomous Robots | Year: 2012
Robotic technology is quickly evolving allowing robots to perform more complex tasks in less structured environments with more flexibility and autonomy. Heterogeneous multi-robot teams are more common as the specialized abilities of individual robots are used in concert to achieve tasks more effectively and efficiently. An important area of research is the use of robot teams to perform modular assemblies. To this end, this paper analyzed the relative performance of two robots with different morphologies and attributes in performing an assembly task autonomously under different coordination schemes using force sensing through a control basis approach. A rigid, point-to-point manipulator and a dual-armed pneumatically actuated hu-manoid robot performed the assembly of parts under a traditional "push-hold" coordination scheme and a human-mimicked "push-push" scheme. The study revealed that the scheme with higher level of cooperation-the "push-push" scheme-performed assemblies faster and more reliably, lowering the likelihood of stiction phenomena, jamming, and wedging. The study also revealed that in "push-hold" schemes industrial robots are better pushers and compliant robots are better holders. The results of our study affirm the use of heterogeneous robots to perform hard-to-do as-semblies and also encourage humans to function as holder's when working in concert with a robot assistant for insertion tasks. © 2011 Springer-Verlag.
Kim H.-H.,Kwangwoon University |
Kim D.-J.,Universal Robotics |
Park K.-H.,Kwangwoon University
IEEE Transactions on Industrial Electronics | Year: 2012
This paper deals with vision-based elevator button recognition for a robot arm manipulating elevator buttons. The major difficulties in elevator button recognition are the presence of partial occlusion of the target objects and image clutter caused by specular reflection from mirrorlike walls inside the elevator. As a remedy for the elevator button recognition problem in highly complicated settings, we propose a robust button recognition algorithm, which is modularized into feature extraction, initial recognition, and postrefinement modules. In consideration of the diverse button shapes in the form of convex quadrilaterals with rounded button corners, a set of features is specially designed to describe each button contour subject to perspective distortion. A homography-based image transform is also employed for the template matching to achieve a more reliable matching performance. Like a grammar in linguistics, geometric button alignment, plays a key role in the elimination of false positives and the estimation of the missing buttons. The proposed algorithm is extensively tested in a robotic platform to verify effectiveness, robustness, and real-time performance. © 2011 IEEE.