Hands-On Learning Environment and Educational Curriculum on Collaborative Robotics

The objective of this paper is to describe teaching modules developed at Wayne State University integrate collaborative robots into existing industrial automation curricula. This is in alignment with Oakland Community College and WSU’s desire to create the first industry-relevant learning program for the use of emerging collaborative robotics technology in advanced manufacturing systems. The various learning program components will prepare a career-ready workforce, train industry professionals, and educate academicians on new technologies. Preparing future engineers to work in highly automated production, requires proper education and training in CoBot theory and applications. Engineering and Engineering Technology at Wayne State University offer different robotics and mechatronics courses, but currently there is not any course on CoBot theory and applications. To follow the industry needs, a CoBot learning environment program is developed, which involves theory and hands-on laboratory exercises in order to solve many important automaton problems. This material has been divided into 5-modules: (1) Introduce the concepts of collaborative robotics, (2) Collaborative robot mechanisms and controls, (3) Safety considerations for collaborative robotics, (4) Collaborative robot operations and programming, (5) Collaborative robot kinematics and validation. These modules cover fundamental knowledge of CoBots in advanced manufacturing systems technology. Module content has been developed based on input and materials provided by CoBot manufacturers. After completing all modules students must submit a comprehensive engineering report to document all requirements

Designing an AR interface to improve trust in Human-Robots collaboration

In a global, e-commerce marketplace, product customisation is driven towards manufacturing flexibility. Conventional caged robots are designed for high volume and low mix production cannot always comply with the increasing low volume and high customisation requirements. In this scenario, the interest in collaborative robots is growing. A critical aspect of Human-Robot Collaboration (HRC) is human trust in robots. This research focuses on increasing the human confidence and trust in robots by designing an Augmented Reality (AR) interface for HRC. The variable affecting the trust involved in HRC have been estimated. These have been utilised for designing the AR-HRC. The proposed design aims to provide situational awareness and spatial dialog. The AR-HRC developed has been tested on 15 participants which have performed a “pick-and-place” task. The results show that the utilisation of AR in the proposed scenario positively affects the human trust in robot. The human-robot collaboration enhanced by AR are more natural and effective. The trust has been measured through an empirical psychometric method also presented in this paper

Fluid‐driven soft CoboSkin for safer human–robot collaboration: fabrication and adaptation

In human–robot collaboration, the wrapping material on robots is not only required to have the sensing ability to adapt to the external environment but also need to have the function of cushioning the collision between human and robot. Herein, a fluid‐driven soft robot skin with sensing and actuating function is successfully applied to a collaborative robot and working well with the host robot. The skin is an integration of sponge force sensors and pneumatic actuators. By altering the internal air pressure in pneumatic actuators, the developed robot skin can provide more than ten times tunable stiffness and sensitivity. In addition, the skin can reduce the peak force of the collision and achieve the actuating function. Using three‐dimensional printing and computer‐aided design, the skin is fabricated and attached to a collaborative robot conformally. Drawing upon the data acquisition and control system, the experiment for illustrating the applications of the CoboSkin is successfully performed. The skin provides the robot with multi‐functions, which are similar to the human muscle and skin attached to human bones. By mimicking human skin and muscle with tactile sensing function and stiffness tuning function, CoboSkin can enhance the adaptability of the robot to human daily life

Robust Adversarial Reinforcement Learning for Optimal Assembly Sequence Definition in a Cobot Workcell

The fourth industrial (I4.0) revolution encourages automatic online monitoring of all products to achieve zero-defect and high-quality production. In this scenario, collaborative robots, in which humans and robots share the same workspace, are a suitable solution that integrates the precision of a robot with the ability and flexibility of a human. To improve human-robot collaboration, human changeable choices or even non-significant mistakes should be allowed or corrected during work. This paper proposes a robust online optimization of the Dassembly sequence through Robust Adversaria lReinforcement Learning (RARL), where an artificial agent is deliberately trying to boycott the assembly completion. To demonstrate the applicability of robust human-robot collaborative assembly using adversarial RL, an environment composed of Markov Decision Process (MDP) like grid world is developed and a multi-agent RL approach is integrated. The results of the framework are promising: the robot observation on human activities has been successfully achieved thanks to a penalty-reward system adopted and the alternation of human to robot actions for the wrong terminal state is the one pursued by the human, but due to robot blockage wrong actions, the right terminal state is followed by human, which is the same as the robot target

EEG-Based Empathic Safe Cobot

An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were distributed to the subjects, and confirmed the results of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10−3). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10−3). In conclusion, within the isolated and constrained experimental environment, the results were satisfactory

Understanding vulnerabilities in cyber physical production systems

Development of future manufacturing systems is featured with flexibility, mass customization, intelligence and context based learning to produce smart products. These production systems are characterized through networked, cooperating objects called cyber physical systems (CPSs). From the manufacturing perspective, the ability to communicate data and develop interaction between devices, manufacturing machinery, raw materials, working robots, humans and the plant environment develops the concept of cyber physical production systems (CPPS). Human-robot collaboration is a technology area that will be an integrated part of the future factory floor and the CPPS. With the involvement of human part in the automated system industrial scenarios, practical safety issues are expected to arise in the connected environment due to the use of a large number of devices, sensors, and cloud services causing complex network, IP conflicts, compromised nodes and communication issues. This all may lead to occupational safety issues on the factory floor in different ways and combinations. Overall, the system's physical vulnerability will be increased in the context of compromised connected working space and cyber-security. In this paper, the authors developed a risk assessment based on system vulnerability of a CPPS developed for a use case requirement and performed a simulated approach by launching a cyber-attack and measuring the causal effect to identify implications on human worker safety

Design and manufacturing of WAAM parts to consolidate new R+D metal AM capabilities at CIM UPC's pilot plant

La fabricació additiva i la robòtica són dues tecnologies que han experimentat una evolució impressionant en els darrers anys. Quan es combinen, permeten resoldre nombroses tasques industrials en diversos camps com l'aeroespacial, l'automòbil o qualsevol sector que requereixi una fabricació o modificació precisa d'una peça. Proporciona un procés d'implementació ràpid, una programació robòtica fàcil i un ús òptim de la cinemàtica del robot per a un control de moviment superior. La fabricació additiva amb arc de filferro (WAAM) és una de les tècniques d'impressió 3D que s'utilitza per fabricar peces metàl·liques utilitzant un arc elèctric, y està en constant evolució. El tema de la meva investigació és establir una manera d'imprimir una peça de plàstic d'una forma desitjada simulant la tecnologia WAAM i realitzar proves mecàniques en les mostres impreses per comparar-les amb productes fabricats de manera convencional del mateix tipus. Per fer-ho, vaig fer servir un cobot UR10e de Universal Robot, combinat amb una eina d'impressió de filament de plàstic d'una impressora 3D Epsilon W27 de BCN3D. L'ús del filament de plàstic és el punt de partida d'un projecte futur que es centra després en l'ús del filament metàl·lic per imprimir peces. Els experiments han portat a una sèrie d'intents d'impressió, estudiant un paràmetre d'impressió a la vegada. La primera sèrie no va portar a impressió reeixides a causa de la distància entre capes i entre passes eren massa grans, portant a discontinuïtats en la trajectòria d'eina i acabat de superfície pobre. Per a la següent sèrie, les opcions d'impressió van ser optimitzades, i les peces impreses van ser molt més precises.La fabricación aditiva y la robótica son dos tecnologías que han experimentado una evolución impresionante en los últimos años. Cuando se combinan, permiten resolver numerosas tareas industriales en varios campos como el aeroespacial, la automoción o cualquier sector que requiera una fabricación o modificación precisa de una pieza. Proporciona un proceso de implementación rápido, una programación robótica fácil y un uso óptimo de la cinemática del robot para un control de movimiento superior. La fabricación aditiva con arco eléctrico (WAAM) es una de las técnicas de impresión 3D que se utiliza para fabricar piezas metálicas, y está en constante evolución. El tema de mi investigación es establecer una manera de imprimir una pieza de plástico de una forma deseada simulando la tecnología WAAM usando un robot colaborativo y realizar pruebas mecánicas en las muestras impresas para compararlas con productos fabricados de manera convencional del mismo tipo. Para ello, utilicé un cobot UR10e de Universal Robot, combinado con una herramienta de impresión de filamento de plástico de una impresora 3D Epsilon W27 de BCN3D. El uso del filamento de plástico es el punto de partida de un proyecto futuro que se centra luego en el uso del filamento metálico para imprimir piezas. Los experimentos han llevado a una serie de intentos de impresiones, estudiando un parámetro de impresión a la vez. La primera serie no dio lugar a impresiones exitosas debido a que la distancia entre capas y entre pasadas era demasiado grande, lo que causaba discontinuidades en la trayectoria de la herramienta y un acabado superficial pobre. Para la serie siguiente, las configuraciones de impresión se optimizaron y las piezas impresas fueron mucho más precisas.Additive manufacturing and robotics are two technologies which have undergone a dazzling evolution over the last few years. When combined, they allow the resolution of numerous industrial tasks in various fields such as aerospace, automobile, or any sector that requires a precise manufacturing or modification of a workpiece. It provides a fast process implementation, an easy robotic programming, and an optimal use of the robot’s kinematics for superior motion control. Wire arc additive manufacturing (WAAM) is one of the 3D printing techniques that is used to manufacture metallic parts using an electric arc, and it is in constant evolution. The subject of my research is about setting up a way to print a plastic part of a desired shape simulating the WAAM technology with a collaborative robot and perform mechanical tests on the printed samples to compare them with conventional manufactured products of the same kind. To do so, I used a UR10e cobot from Universal Robot, combined with a plastic filament printing toolhead of an Epsilon W27 3D printer from BCN3D. The use of plastic filament is the starting point of a future project focusing then on the use of metallic filament to print parts. The experiments have led to a series of attempts of prints, studying a parameter of impression at a time. The first series did not lead to successful prints because of the distance between layers and between passes were too big, leading to discontinuities in the toolpath and poor surface finish. For the following series, the printing settings were optimized, and the printed pieces were much more accurate.Incomin