A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks

Robotic assistance for industrial sanding with a smooth approach to the surface and boundary constraints

Surface treatment operations, such as sanding, deburring, finishing, grinding, polishing, etc. are progressively becoming more automated using robotic systems. However, previous research in this field used a completely automatic operation of the robot system or considered a low degree of human-robot interaction. Therefore, to overcome this issue, this work develops a truly synergistic cooperation between the human operator and the robot system to get the best from both. In particular, in the application developed in this work the human operator provides flexibility, guiding the tool of the robot system to treat arbitrary regions of the workpiece surface; while the robot system provides strength, accuracy and security, not only holding the tool and keeping the right tool orientation, but also guaranteeing a smooth approach to the workpiece and confining the tool within the allowed area close to the workpiece. Moreover, to add more flexibility to the proposed method, when the user is not guiding the robot tool, a robot automatic operation is activated to perform the treatment in prior established regions. Furthermore, a camera network is used to get a global view of the robot workspace in order to obtain the workpiece location accurately and in real-time. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm

Governance in Hospitals - The Case of Business Process Alignment

Nowadays, health care organizations are situated in a highly competitive and selective market. They are forced to guarantee high quality and cost-efficient care. On the one hand, process management in health care is linked to the economical aspect, which means the reduction of time and cost efforts. On the other hand, it is also touched by the issue of governance of health care process according to the evidence medicine. The paper contributes to this second aspect. The research question is, how hospitals can be supported in building evidence based clinical processes. A method is presented, which is based on the two central instruments in this context, the Clinical Practice Guideline (CPG) that aggregates evident medical knowledge, and the Clinical Pathway (CP) that describes the clinic-specific processes for defined patient groups. The paper demonstrates the role and potential conceptual modeling for clinical process governance using CPG and CPs

Adaptive Tuning of Robotic Polishing Skills based on Force Feedback Model

Acquiring human skills offers an efficient approach to tackle complex task planning challenges. When performing a learned skill model for a continuous contact task, such as robot polishing in an uncertain environment, the robot needs to be able to adaptively modify the skill model to suit the environment and perform the desired task. The environmental perturbation of the polishing task is mainly reflected in the variation of contact force. Therefore, adjusting the task skill model by providing feedback on the contact force deviation is an effective way to meet the task requirements. In this study, a phase-modulated diagonal recurrent neural network (PMDRNN) is proposed for force feedback model learning in the robotic polishing task. The contact between the tool and the workpiece in the polishing task can be considered a dynamic system. In comparison to the existing feedforward neural network phase-modulated neural network (PMNN), PMDRNN combines the diagonal recurrent network structure with the phase-modulated neural network layer to improve the learning performance of the feedback model for dynamic systems. Specifically, data from real-world robot polishing experiments are used to learn the feedback model. PMDRNN demonstrates a significant reduction in the training error of the feedback model when compared to PMNN. Building upon this, the combination of PMDRNN and dynamic movement primitives (DMPs) can be used for real-time adjustment of skills for polishing tasks and effectively improve the robustness of the task skill model. Finally, real-world robotic polishing experiments are conducted to demonstrate the effectiveness of the approach.Comment: This paper has been accepted by The 2023 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2023

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Bioinspired Spike-Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

The brain has great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great interest due to their potential applicability to robotics, although these systems are still a challenge to be solved. This work proposes a spike-based robotic navigation and environment pseudomapping system formed by a bioinspired hippocampal memory model connected to a posterior parietal cortex (PPC) model. The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision-making. This system is implemented on the SpiNNaker hardware platform using spiking neural networks. A set of real-time experiments are applied to demonstrate the correct functioning of the system in virtual and physical environments on a robotic platform. The system is able to navigate through the environment to reach a goal position starting from an initial position, avoiding obstacles and mapping the environment. To the best of the authors’ knowledge, this is the first implementation of an environment pseudomapping system with dynamic learning based on a bioinspired hippocampal memory. © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.Ministerio de Educación, Cultura y Deporte (MECD). España PID2019‐105556GB‐C33Horizonte 2020 (Unión Europea) CHIST‐ERA‐18‐ACAI‐004Horizonte 2020 (Unión Europea) PCI2019‐111841‐2/AEI/10.13039/501100011033Ministerio de Ciencia e Innovación (MCIN) España AEI/10.13039/50110001103

The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation

Model-based training of manual procedures in automated production systems

Maintenance engineers deal with increasingly complex automated production systems (aPSs). Such systems are characterized by an increasing computerization or the addition of robots that collaborate with human workers. The effects of changing or replacing components of such systems are difficult to assess since there are complex interdependencies between process parameters and the state of the components. This paper proposes a model-based training system that visualizes these interdependencies using domain-independent SysML models. The training system consists of a virtual training system for initial training and an online support system for assistance during maintenance or changeover procedures. Both systems use structural SysML models to visualize the state of the machine at a certain step of a procedure. An evaluation of the system in a changeover procedure against a paper-based manual showed promising results regarding effectiveness, usability and attractiveness.Comment: 25 pages, https://www.sciencedirect.com/science/article/pii/S095741581830080