Industrial Robot Skills

When robots are working in dynamic environments, close to humans lacking extensive knowledge of robotics, there is a strong need to simplify the user interaction and make the system execute as autonomously as possible. For industrial robots working side-by-side with humans in manufacturing industry, AI systems are necessary to lower the demand on programming time and expertise. One central concept in knowledge modeling for robots is action representation. In this paper, we describe our representation of robot skills. The skills have resource requirements, logical and procedural information from which executable code can be generated

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts

This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bimanual sewing module adopts a learning-by-demonstration approach to transfer human hand-sewing skills to the robots. The human demonstrations were firstly observed by the vision module and then encoded using a statistical model to generate the reference motion trajectories. During autonomous robot sewing, the vision module plays the role of coordinating multi-robot collaboration. Experiment results show that the robots can adapt to generalized stent designs. The proposed system can also be used for other manipulation tasks, especially for flexible production of customized products and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial Informatics, Key words: modularity, medical device customization, multi-robot system, robot learning, visual servoing, robot sewin

Design and implementation of robot skill programming and control

Abstract. Skill-based approach has been represented as a solution to the raising complicity of robot programming and control. The skills rely heavily on the use of sensors integrating sensor perceptions and robot actions, which enable the robot to adapt to changes and uncertainties in the real world and operate autonomously. The aim of this thesis was to design and implement a programming concept for skill-based control of industrial robots. At the theoretical part of this thesis, the industrial robot system is introduced as well as some basic concepts of robotics. This is followed by the introduction of different robot programming and 3D machine vision methods. At the last section of the theoretical part, the structure of skill-based programs is presented. In the experimental part, structure of the skills required for the “grinding with localization” -task are presented. The task includes skills such as global localization with 3D-depth sensor, scanning the object with 2D-profile scanner, precise localization of the object as well as two grinding skills: level surface grinding and straight seam grinding. Skills are programmed with an off-line programming tool and implemented in a robot cell, composed of a standard industrial robot with grinding tools, 3D-depth sensors and 2D-profile scanners. The results show that global localization can be carried out with consumer class 3D-depth sensors and more accurate local localization with an industrial high-accuracy 2D-profile scanner attached to the robot’s flange. The grinding experiments and tests were focused on finding suitable structures of the skill programs as well as to understand how the different parameters influence on the quality of the grinding.Robotin taitopohjaisten ohjelmien ohjelmointi ja testaus. Tiivistelmä. Robotin taitopohjaisia ohjelmia on esitetty ratkaisuksi robottien jatkuvasti monimutkaistuvaan ohjelmointiin. Taidot pohjautuvat erilaisten antureiden ja robotin toimintojen integroimiseen, joiden avulla robotti pystyy havainnoimaan muutokset reaalimaailmassa ja toimimaan autonomisesti. Tämän työn tavoitteena oli suunnitella ja toteuttaa taitopohjaisia ohjelmia teollisuusrobotille. Aluksi työn teoriaosuudessa esitellään teollisuusrobottijärjestelmään kuuluvia osia ja muutamia robotiikan olennaisimpia käsitteitä. Sen jälkeen käydään läpi eri robotin ohjelmointitapoja ja eri 3D-konenäön toimintaperiaatteita. Teoriaosuuden lopussa esitellään taitopohjaisten ohjelmien rakennetta. Käytännön osuudessa esitellään ”hionta paikoituksella” -tehtävän suoritukseen tarvittavien taitojen rakenne. Tehtävän vaatimia taitoja ovat muun muassa kappaleen globaalipaikoitus 3D-syvyyskameralla, kappaleen skannaus 2D-profiiliskannerilla, kappaleen tarkkapaikoitus ja kaksi eri hiontataitoa: tasomaisen pinnan ja suoran sauman hionta. Taidot ohjelmoidaan off-line ohjelmointityökalulla ja implementoidaan robottisoluun, joka muodostuu hiontatyökaluilla varustetusta teollisuusrobotista, 3D-kameroista ja 2D-profiiliskannereista. Työn tuloksista selviää, että kappaleen globaalipaikoitus voidaan suorittaa kuluttajille suunnatuilla 3D-syvyyskameroilla ja kappaleen tarkempi lokaalipaikoitus robotin ranteeseen kiinnitetyllä teollisuuden käyttämillä 2D-profiiliskannereilla. Hiontojen kokeellisessa osuudessa etsitään ohjelmien oikeanlaista rakennetta sekä muodostetaan käsitys eri parametrien vaikutuksesta hionnan laatuun

MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Industrial manipulator based intelligent assist system for human-robot cooperative assembly tasks

Industrial robot manipulators are widely being used for various automated tasks such as pick and place, welding, painting, palletizing, drilling, etc. in standardized industrial processes that require monotonic execution of preprogrammed repetitive tasks with high precision and/or productivity. However, in many operations, it is desirable to exploit the force capabilities of robots by directly combining them with the skills and incomparable sensomotoric abilities of a human being for complex tasks

Experiences on a motivational learning approach for robotics in undergraduate courses

This paper presents an educational experience carried out in robotics undergraduate courses from two different degrees: Computer Science and Industrial Engineering, having students with diverse capabilities and motivations. The experience compares two learning strategies for the practical lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems like robot motion, localization, and mapping, while the second strategy opts for using the ROS framework for the development of algorithms facing a competitive challenge, e.g. exploration algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic related) professional careers, which enhanced their disposition to study it. They also considered that the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a greater extent than the skeleton-code based ones. These and other conclusions will be useful in posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach