Collaborative human-machine interfaces for mobile manipulators.

The use of mobile manipulators in service industries as both agents in physical Human Robot Interaction (pHRI) and for social interactions has been on the increase in recent times due to necessities like compensating for workforce shortages and enabling safer and more efficient operations amongst other reasons. Collaborative robots, or co-bots, are robots that are developed for use with human interaction through direct contact or close proximity in a shared space with the human users. The work presented in this dissertation focuses on the design, implementation and analysis of components for the next-generation collaborative human machine interfaces (CHMI) needed for mobile manipulator co-bots that can be used in various service industries. The particular components of these CHMI\u27s that are considered in this dissertation include: Robot Control: A Neuroadaptive Controller (NAC)-based admittance control strategy for pHRI applications with a co-bot. Robot state estimation: A novel methodology and placement strategy for using arrays of IMUs that can be embedded in robot skin for pose estimation in complex robot mechanisms. User perception of co-bot CHMI\u27s: Evaluation of human perceptions of usefulness and ease of use of a mobile manipulator co-bot in a nursing assistant application scenario. To facilitate advanced control for the Adaptive Robotic Nursing Assistant (ARNA) mobile manipulator co-bot that was designed and developed in our lab, we describe and evaluate an admittance control strategy that features a Neuroadaptive Controller (NAC). The NAC has been specifically formulated for pHRI applications such as patient walking. The controller continuously tunes weights of a neural network to cancel robot non-linearities, including drive train backlash, kinematic or dynamic coupling, variable patient pushing effort, or slope surfaces with unknown inclines. The advantage of our control strategy consists of Lyapunov stability guarantees during interaction, less need for parameter tuning and better performance across a variety of users and operating conditions. We conduct simulations and experiments with 10 users to confirm that the NAC outperforms a classic Proportional-Derivative (PD) joint controller in terms of resulting interaction jerk, user effort, and trajectory tracking error during patient walking. To tackle complex mechanisms of these next-gen robots wherein the use of encoder or other classic pose measuring device is not feasible, we present a study effects of design parameters on methods that use data from Inertial Measurement Units (IMU) in robot skins to provide robot state estimates. These parameters include number of sensors, their placement on the robot, as well as noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). The results from that study facilitate the creation of robot skin, and in order to enable their use in complex robots, we propose a novel pose estimation method, the Generalized Common Mode Rejection (GCMR) algorithm, for estimation of joint angles in robot chains containing composite joints. The placement study and GCMR are demonstrated using both Gazebo simulation and experiments with a 3-DoF robotic arm containing 2 non-zero link lengths, 1 revolute joint and a 2-DoF composite joint. In addition to yielding insights on the predicted usage of co-bots, the design of control and sensing mechanisms in their CHMI benefits from evaluating the perception of the eventual users of these robots. With co-bots being only increasingly developed and used, there is a need for studies into these user perceptions using existing models that have been used in predicting usage of comparable technology. To this end, we use the Technology Acceptance Model (TAM) to evaluate the CHMI of the ARNA robot in a scenario via analysis of quantitative and questionnaire data collected during experiments with eventual uses. The results from the works conducted in this dissertation demonstrate insightful contributions to the realization of control and sensing systems that are part of CHMI\u27s for next generation co-bots

Modeling Human Motor Skills to Enhance Robots’ Physical Interaction

The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a research approach that moves from the understanding of human movements and derives usefull guidelines for the planning of arm movements and the learning of skills for physical interaction of robots with the surrounding environment

A gesture-based robot program building software

With the advent of intelligent systems, industrial workstations and working areas have undergone a revolution. The increased need for automation is satisfied using high-performance industrial robots in fully automated workstations. In the manufacturing industry, sophisticated tasks still require human intervention in completely manual workstations, even if at a slower production rate. To improve the efficiency of manual workstations, Collaborative Robots (Co-Bots) have been designed as part of the Industry 4.0 paradigm. These robots collaborate with humans in safe environments to support the workers in their tasks, thus achieving higher production rates compared to completely manual workstations. The key factor is that their adoption relieves humans from stressful and heavy operations, decreasing job-related health issues. The drawback of Co-Bots stands in their design: to work side-by-side with humans they must guarantee safety; thus, they have very strict limitations on their forces and velocities, which limits their efficiency, especially when performing non-trivial tasks. To overcome these limitations, our idea is to design Meta-Collaborative workstations (MCWs), where the robot can operate behind a safety cage, either physical or virtual, and the operator can interact with the robot, either industrial or Collaborative, by means of the same communication channel. Our proposed system has been developed to easily build robot programs purposely designed for MCWs, based on (i) the recognition of hand gestures (using a vision-based communication channel) and (ii) ROS to carry out communication with the robot

Information knowledge and technology for Development in Africa

Information, knowledge, and technology occupy significant space in the information and knowledge society and ongoing debates on development such as sustainable development goals (SDGs) agenda 2030 and the fourth industrial revolution (4IR). Disruptive technologies and cyber-physical systems, obscuring the lines between the physical, digital and biological, escalated by the COVID-19 pandemic, present a ‘new normal’ that profoundly affects the nature and magnitude of responses required to sustain and benefit from the new developments. Africa, known for late adoption of new technologies and innovations, is leapfrogging development stages in several enviable ways. This book, Information knowledge and technology for development in Africa’, written by eminent African scholars, comprises chapters that satisfactorily address information access, artificial intelligence, information ethics, e-learning, library and information science education (LISE) in the 4IR, data literacy and e-scholarship, and knowledge management, which are increasingly essential for information access, services, and LISE in Africa. We expect the book to support research, teaching and learning in African higher education and worldwide for comparative scholarship

Responsible AI in Africa

This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa. This is an open access book

Application-driven visual computing towards industry 4.0 2018

245 p.La Tesis recoge contribuciones en tres campos: 1. Agentes Virtuales Interactivos: autónomos, modulares, escalables, ubicuos y atractivos para el usuario. Estos IVA pueden interactuar con los usuarios de manera natural.2. Entornos de RV/RA Inmersivos: RV en la planificación de la producción, el diseño de producto, la simulación de procesos, pruebas y verificación. El Operario Virtual muestra cómo la RV y los Co-bots pueden trabajar en un entorno seguro. En el Operario Aumentado la RA muestra información relevante al trabajador de una manera no intrusiva. 3. Gestión Interactiva de Modelos 3D: gestión online y visualización de modelos CAD multimedia, mediante conversión automática de modelos CAD a la Web. La tecnología Web3D permite la visualización e interacción de estos modelos en dispositivos móviles de baja potencia.Además, estas contribuciones han permitido analizar los desafíos presentados por Industry 4.0. La tesis ha contribuido a proporcionar una prueba de concepto para algunos de esos desafíos: en factores humanos, simulación, visualización e integración de modelos

Study of the application of a collaborative robot for machining tasks

[EN] The importance of collaborative robots is increasing very fast in the industry. They have several advantages over the 'classical' robot arms: they may work side-by-side with humans, their environment needs less adaptation, they may be easily transported, etc. Their joints are more elastic than those in classical robots. For this reason, they are less suited for machining. In this work, a collaborative robot, a sensor of 6 Degree of Freedom (DOF) and a spindle with flex-shaft attachment are used to perform milling operations on soft materials. An inner/outer loop control is being developed to control the movements and the cutting forces. The experiments have been designed to evaluate the capability of the robot with milling operations with different parameters. An analysis of the dimensions and the finished surface will be carried out. The contribution of this article is to determine the possibilities and limitations of the collaborative robots in machining applications, with external control of forces.The authors are grateful for the financial support of the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE). This work was funded by the CONICYT PFCHA/DOCTORADO BECAS CHILE/2017 - 72180157Pérez-Ubeda, R.; Gutiérrez, SC.; Zotovic Stanisic, R.; Lluch-Cerezo, J. (2019). Study of the application of a collaborative robot for machining tasks. Procedia Manufacturing. 41:867-874. https://doi.org/10.1016/j.promfg.2019.10.009S86787441International Federation of Robotics, IFR forecast: 1.7 million new robots to transform the world´s factories by 2020, IFR. (2017). https://ifr.org/ifr-press-releases/news/ifr-forecast-1.7-million-new-robots-to-transform-the-worlds-factories-by-20 (accessed February 15, 2019).Robotic Industries Association (RIA), Top 6 Future Trends in Robotic Automation, RIA. (2018). https://www.robotics.org/blog-article.cfm/Top-6-Future-Trends-in-Robotic-Automation/101 (accessed May 6, 2019).A. Grau, M. Indri, L. Lo Bello, T. Sauter, Industrial robotics in factory automation: From the early stage to the Internet of Things, in: Proc. IECON 2017 - 43rd Annu. Conf. IEEE Ind. Electron. Soc., 2017: pp. 6159–6164. doi:10.1109/IECON.2017.8217070.Hui Zhang, Jianjun Wang, G. Zhang, Zhongxue Gan, Zengxi Pan, Hongliang Cui, Zhenqi Zhu, Machining with flexible manipulator: toward improving robotic machining performance, in: Proceedings, 2005 IEEE/ASME Int. Conf. Adv. Intell. Mechatronics., IEEE, 2005: pp. 1127–1132. doi:10.1109/AIM.2005.1511161.Klimchik, A., Ambiehl, A., Garnier, S., Furet, B., & Pashkevich, A. (2017). Efficiency evaluation of robots in machining applications using industrial performance measure. Robotics and Computer-Integrated Manufacturing, 48, 12-29. doi:10.1016/j.rcim.2016.12.005Iglesias, I., Sebastián, M. A., & Ares, J. E. (2015). Overview of the State of Robotic Machining: Current Situation and Future Potential. Procedia Engineering, 132, 911-917. doi:10.1016/j.proeng.2015.12.577U. Robots, An introduction to common collaborative robot applications, White Pap. (2018) 18. https://info.universal-robots.com/common-collaborative-robot-applications (accessed September 23, 2018).R. Perez, S.C. Gutierrez Rubert, R. Zotovic, A Study on Robot Arm Machining: Advance and Future Challenges, in: 29TH DAAAM Int. Symp. Intell. Manuf. Autom., 2018: pp. 0931–0940. doi:10.2507/29th.daaam.proceedings.134.Chen, S., & Zhang, T. (2018). Force control approaches research for robotic machining based on particle swarm optimization and adaptive iteration algorithms. Industrial Robot: An International Journal, 45(1), 141-151. doi:10.1108/ir-03-2017-0045B. Siciliano, Robotics: Modelling, Planning and Control (2nd edition), 2010. doi:10.1007/978-1-84628-642-1

Responsible AI in Africa

This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa. This is an open access book