AUTOMATIC SIMULATION-BASED DESIGN AND VALIDATION OF ROBOTIC GRIPPER FINGERS
Aswin K Ramasubramanian, Matthew Connolly, Robins Mathew, Nikolaos Papakostas
2022
The design of robotic gripper fingers is a complex process and often requires significant effort and time. This paper investigates a method to automatically generate new iterations of the gripper finger design as well as to validate its performance in a simulation environment. A Computer-Aided Design (CAD) software platform and a physics-based simulation framework are deployed to work in tandem to redesign and validate an initial gripper finger design aiming at reducing the overall time and cost required for physical validation. The proposed approach is validated in a real robotic case scenario, performing a series of pick and place tasks.
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MODEL PREDICTIVE IMPEDANCE CONTROL WITH GAUSSIAN PROCESSES FOR HUMAN AND ENVIRONMENT INTERACTION
Kevin Haninger, Christian Hegeler, Luka Peternel
August 2022
In tasks where the goal or configuration varies between iterations, human-robot interaction (HRI) can allow the robot to handle repeatable aspects and the human to provide information which adapts to the current state. Advanced interactive robot behaviors are currently realized by inferring human goal or, for physical interaction, adapting robot impedance. While many application-specific heuristics have been proposed for interactive robot behavior, they are often limited in scope, e.g. only considering human ergonomics or task performance. To improve generality, this paper proposes a framework which plans both trajectory and impedance online, handles a mix of task and human objectives, and can be efficiently applied to a new task. This framework can consider many types of uncertainty: contact constraint variation, uncertainty in human goals, or task disturbances. An uncertainty-aware task model is learned from a few demonstrations using Gaussian Processes. This task model is used in a nonlinear model predictive control (MPC) problem to optimize robot trajectory and impedance according to belief in discrete human goals, human kinematics, safety constraints, contact stability, and frequency-domain disturbance rejection. This MPC formulation is introduced, analyzed with respect to convexity, and validated in co-manipulation with multiple goals, a collaborative polishing task, and a collaborative assembly task.
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DIGITAL TWIN FOR HUMAN–ROBOT COLLABORATION IN MANUFACTURING: REVIEW AND OUTLOOK
Aswin K Ramasubramanian, Robins Mathew, Matthew Kelly, Vincent Hargaden, Nikolaos Papakostas
2022
Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system.
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REVIEW AND APPLICATION OF EDGE AI SOLUTIONS FOR MOBILE COLLABORATIVE ROBOTIC PLAFORMS
Aswin K Ramasubramanian, Robins Mathew, Inder Preet, Nikolaos Papakostas
2022
Edge AI technologies have emerged as a powerful solution for gathering and processing data in real-time, supporting the development of advanced applications for process monitoring, planning and control. Edge AI devices coupled with different sensory systems can be used for facilitating the synergetic human-robot collaboration at the shop floor level. This paper reviews edge AI devices and solutions that may potentially support the operation of cells or workstations comprising human operators and mobile robotic platforms. The use of edge AI-capable devices for operator tracking in real-world applications and the potential of edge AI technologies towards developing and deploying process digital twins are discussed. The main goal is to investigate how edge AI solutions may improve the performance of processes where a collaborative mobile manipulator works alongside a human operator in a safe and reliable manner. Lastly, the use of advanced communication frameworks, such as the Robot Operating System (ROS), is discussed in the context of supporting the seamless interaction between a collaborative mobile platform and Edge AI devices.
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IMPEDANCE ADAPTATION BY REINFORCEMENT LEARNING WITH CONTACT DYNAMIC MOVEMENT PRIMITIVES
Chunyang Chang, Kevin Haninger, Yunlei Shi, Chengjie Yuan, Zhaopeng Chen, Jianwei Zhang
March 2022
Dynamic movement primitives (DMPs) allow complex position trajectories to be efficiently demonstrated to a robot. In contact-rich tasks, where position trajectories alone may not be safe or robust over variation in contact geometry, DMPs have been extended to include force trajectories. However, different task phases or degrees of freedom may require the tracking of either position or force — e.g., once contact is made, it may be more important to track the force demonstration trajectory in the contact direction. The robot impedance balances between following a position or force reference trajectory, where a high stiffness tracks position and a low stiffness tracks force. This paper proposes using DMPs to learn position and force trajectories from demonstrations, then adapting the impedance parameters online with a higher-level control policy trained by reinforcement learning. This allows one-shot demonstration of the task with DMPs, and improved robustness and performance from the impedance adaptation. The approach is validated on peg-in-hole and adhesive strip application tasks.
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TOWARDS HIGH-PAYLOAD ADMITTANCE CONTROL FOR MANUAL GUIDANCE WITH ENVIRONMENTAL CONTACT
Kevin Haninger, Marcel Radke, Axel Vick, Jörg Krüger
February 2022
Force control enables hands-on teaching and physical collaboration, with the potential to improve ergonomics and flexibility of automation. Established methods for the design of compliance, impedance control, and collision response can achieve free-space stability and acceptable peak contact force on lightweight, lower payload robots. Scaling collaboration to higher payloads can allow new applications, but introduces challenges due to the more significant payload dynamics and the use of higher-payload industrial robots. To achieve high-payload manual guidance with contact, this paper proposes and validates new mechatronic design methods: standard admittance control is extended with damping feedback, compliant structures are integrated to the environment, and a contact response method which allows continuous admittance control is proposed. These methods are compared with respect to free-space stability, contact stability, and peak contact force. The resulting methods are then applied to realize two contact-rich tasks on a 16 kg payload (peg in hole and slot assembly) and free-space co-manipulation of a 50 kg payload.
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USING A PROCESS SIMULATION PLATFORM FOR REVIEWING AUTOMATED AIRPORT BAGGAGE HANDLING SYSTEM CONFIGURATIONS
Barry Fay, Aswin K Ramasubramanian, Rónán Dillon Murphy, Tadhg Adderley, Nikolaos Papakostas
October 2022
The use of digital manufacturing platforms is becoming increasingly important since they facilitate process visualisation, optimisation, and validation, allowing engineers to make informed decisions at early product and process development phases. In this paper, the potential of using process simulation platforms is explored in order to visualise and validate baggage handling processes involving robotic handling systems and human operators. Multiple baggage handling layout configurations are digitally constructed and reviewed with the aim to investigate cost, time, and flexibility performance indicators. Furthermore, the advantages of using digital manufacturing and process simulation platforms are discussed.
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AN ADMITTANCE BASED HIERARCHICAL CONTROL FRAMEWORK FOR DUAL-ARM COBOTS
Sonny Tarbouriech, Benjamin Navarro, PhilippeFraisse, André Crosnier, Andrea Cherubini, Damien Sallé
October 2022
Dual-arm robotic platforms have solid arguments to match the growing need for versatility in the industry. Coupling the control of two manipulators for cooperative purposes enlarges the scope of feasible operations, while adding perception capabilities allows to navigate in dynamic environments. In this respect, we propose a complete online kinematic control framework for dual-arm robots operating in unstructured industrial settings. We base our approach on admittance control in the cooperative task space. Regulating internal and external efforts offer safe bimanual task execution and enables physical interaction. We implement a hierarchical quadratic programming architecture that applies a prioritization of tasks: most efforts are concentrated on the proper tracking of relative motions of the arms, which is the most critical for safety reasons. We demonstrate the performance of our framework through a “teaching-by-demonstration” experiment on the dual-arm mobile cobot BAZAR.
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CONTACT INFORMATION FLOW AND DESIGN OF COMPLIANCE
Kevin Haninger, Marcel Radke, Richard Hartisch, Jörg Krüger
2022
Identifying changes in contact during contact-rich manipulation can detect task state or errors, enabling improved robustness and autonomy. The ability to detect contact is affected by the mechatronic design of the robot, especially its physical compliance. Established methods can design physical compliance for many aspects of contact performance (e.g. peak contact force, motion/force control bandwidth), but are based on time-invariant dynamic models. A change in contact mode is a discrete change in coupled robot-environment dynamics, not easily considered in existing design methods.Towards designing robots which can robustly detect changes in contact mode online, this paper investigates how mechatronic design can improve contact estimation, with a focus on the impact of the location and degree of compliance. A design metric of information gain is proposed which measures how much position/force measurements reduce uncertainty in the contact mode estimate. This information gain is developed for fully- and partially-observed systems, as partial observability can arise from joint flexibility in the robot or environmental inertia. Hardware experiments with various compliant setups validate that information gain predicts the speed and certainty with which contact is detected in (i) monitoring of contact-rich assembly and (ii) collision detection.
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FLEXURE-BASED ENVIRONMENTAL COMPLIANCE FOR HIGH-SPEED ROBOTIC CONTACT TASKS
Richard Matthias Hartisch, Kevin Haninger
2022
The design of physical compliance – its location, degree, and structure – affects robot performance and robustness in contact-rich tasks. While compliance is often used in the robot’s joints, flange, or end-effector, this paper proposes compliant structures in the environment, allowing safe and robust contact while keeping the higher motion control bandwidth and precision of high impedance robots. Compliance is here realized with flexures and viscoelastic materials, which are integrated to several mechanisms to offer structured compliance, such as a remote center of compliance. Additive manufacturing with fused deposition modeling is used, allowing faster design iteration and low-cost integration with standard industrial equipment. Mechanical properties, including the total stiffness matrix, stiffness ratio, and rotational precision, are analytically determined and compared to experimental results. Three remote center of compliance (RCC) devices are prototyped and tested in high-speed assembly tasks.
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MODEL PREDICTIVE CONTROL WITH GAUSSIAN PROCESSES FOR FLEXIBLE MULTI-MODAL PHYSICAL HUMAN ROBOT INTERACTION
Kevin Haninger, Christian Hegeler, Luka Peternel
2022
Physical human-robot interaction can improve human ergonomics, task efficiency, and the flexibility of automation, but often requires application-specific methods to detect human state and determine robot response. At the same time, many potential human-robot interaction tasks involve discrete modes, such as phases of a task or multiple possible goals, where each mode has a distinct objective and human behavior. In this paper, we propose a novel method for multi-modal physical human-robot interaction that builds a Gaussian process model for human force in each mode of a collaborative task. These models are then used for Bayesian inference of the mode, and to determine robot reactions through model predictive control. This approach enables optimization of robot trajectory based on the belief of human intent, while considering robot impedance and human joint configuration, according to ergonomic- and/or task-related objectives. The proposed method reduces programming time and complexity, requiring only a low number of demonstrations (here, three per mode) and a mode-specific objective function to commission a flexible online human-robot collaboration task. We validate the method with experiments on an admittance-controlled robot, performing a collaborative assembly task with two modes where assistance is provided in full six degrees of freedom. It is shown that the developed algorithm robustly re-plans to changes in intent or robot initial position, achieving online control at 15 Hz.
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REALISTIC SIMULATION OF ROBOTIC GRASPING TASKS: REVIEW AND APPLICATION
Matthew Connolly, Aswin K Ramasubramanian, Matthew Kelly, Jack McEvoy, Nikolaos Papakostas
2021
Robots have developed into highly capable machines that currently constitute an integral part of many manufacturing environments. The use of simulation platforms for designing and validating robotic processes are becoming increasingly important since they may lead to the faster design and commissioning of robotic solutions, without disrupting production for long periods of time. In this paper, two simulation platforms are reviewed in the context of relatively simple robotic grasping tasks with the goal to explore how accurate and realistic the resulting models can be. The main expectations regarding the future and further evolution of robotic simulation platforms are outlined.
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SEAMLESS HUMAN-ROBOT COLLABORATIVE ASSEMBLY USING ARTIFICIAL INTELLIGENCE AND WEARABLE DEVICES
Nikos Dimitropoulos, Theodoros Togias, Natalia Zacharaki, George Michalos and Sotiris Makris
June 2021
Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator’s needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system.
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DUAL ARM CO-MANIPULATION ARCHITECTURE WITH ENHANCED HUMAN-ROBOT COMMUNICATION FOR LARGE PART MANIPULATION
Aitor Ibarguren, Iveta Eimontaite, José Luis Outón and Sarah Fletcher
October 2020
The emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.
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PRELIMINARY DEVELOPMENT OF THE PSYCHOLOGICAL FACTORS ASSESSMENT FRAMEWORK FOR MANUFACTURING HUMAN-ROBOT COLLABORATION
Iveta Eimontaitre and Sarah Fletcher
2020
Robots, although not new in manufacturing, are still only just being directly integrated with human operators. Although timely and measured human factors integration in technology development can increase its acceptance, the impacts on manufacturing operators are still largely unknown. The proposed work described in this paper discusses the SHERLOCK (seamless and safe human-centred robotic applications for novel collaborative workplace) project approach to human factors integration that aims to develop a standardised tool for evaluating the impacts of robotics in manufacturing (psychological factors assessment framework). Four industrial use case studies of new collaborative applications will allow investigations of changes in operators’ psychological states depending on the robot characteristics and assembly requirements. This analysis will enable the development of the framework, which will allow quicker assessment of psychological factors and recommendations for operator needs and requirements in a variety of manufacturing
applications.
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OPERATOR SUPPORT IN HUMAN-ROBOT COLLABORATIVE ENVIRONMENTS USING AI ENHANCED WEARABLE DEVICES
Nikos Dimitropoulos, Theodoros Togias, George Michalos, Sotiris Makris
2021
Nowadays, in order to cover the needs of market for product mass customization, industries have started to move to hybrid production cells, involving both robots and human operators. Research has been done during previous years to promote and improve the collaboration between humans and robots, trying to address topics such as safety, awareness and cognitive support in form of Augmented Reality based instructions. Results of previous research show bottlenecks related to the way of interaction of the operators with such supportive systems though. Direct interaction approach with the use of push buttons or indirect-gesture based interaction, which are most often adopted by the researchers, require operators to constantly occupy their hands performing the relevant button presses or gestures. Moreover, previous approaches are hardware dependent and need a lot of customization to work with different hardware. This work tries to address these bottlenecks proposing the usage of wearable devices enhanced with AI in order to support the interaction of human operators with robots in human-robot collaborative environments in a seamless and non-intrusive way, wrapped around a framework called “Operator Support Module” (OSM). Among others, OSM supports a variety of hardware to easily fit in various industrial scenarios. Two case studies will be presented to demonstrate the approach.
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AN APPROACH FOR TASK AND ACTION PLANNING IN HUMAN-ROBOT COLLABORATIVE CELLS USING AI
George Evangelou, Nikos Dimitropoulos, George Michalos, Sotiris Makris
2021
Human–Robot Collaborative (HRC) workcells aim to elevate the conventional industrial lines, by enabling seamless interaction between operators and machines. Shifting away from standard (mainly static) workcells, with operators and machines following strictly defined limits and schedules, the new era of manufacturing introduces versatile workspaces, shared between the manufacturing resources. The non-deterministic workflow of such workspaces raises concerns over planning of task and actions among the resources, as the workcell is now a dynamic environment.
Following these demands, this study introduces a solution in scheduling and assignment of assembly tasks to both human and robot resources, aiming for effective emergence of alternative task-resource assignment sequences. The proposed framework is optimized to provide a human-centered approach on the assembly line and alleviate human operators from exhaustive or unsafe tasks, while accounting for environment changes, such as positioning of resources and parts.
This tool was used in a use case from the industrial modules manufacturing sector.
© 2020 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of CIRP
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AN OUTLOOK ON FUTURE HYBRID ASSEMBLY SYSTEMS – THE SHERLOCK APPROACH
Nikos Dimitropoulos, George Michalos, Sotiris Makris
February 2021
Over the last years both Research and Industry have tried to address the requirement for flexible production by introducing technologies that allow humans and robots to coexist and share production tasks. The focus has been to ensure the safety of humans while interacting with robots. Previous EU funded projects provided proof that humans and robots destiny is collaboration rather than competition. It has been revealed though that Human Robot Collaborative (HRC) applications present drawbacks that limit industrial adoption.
SHERLOCK EU project aims to exploit the lessons learnt and the technical excellence, developing the first high payload collaborative robot (COMAU AURA), dynamically reconfigurable safety monitoring systems and smart Human Robot (HR) interfaces allowing the seamless integration of operators and robots in a common workflow. The aim is to introduce the latest safe robotic technologies including high payload collaborative arms, exoskeletons and mobile manipulators in diverse production environments, enhance them with smart mechatronics and AI based cognition and thus create efficient HRC stations that are designed to be safe and guarantee the acceptance and wellbeing of operators.
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PATH DRIVEN DUAL ARM MOBILE CO-MANIPULATION ARCHITECTURE FOR LARGE PART MANIPULATION IN INDUSTRIAL ENVIRONMENTS
Aitor Ibarguren, Paul Daelman
October 2021
Collaborative part transportation is an interesting application as many industrial sectors require moving large parts among different areas of the workshops, using a large amount of the workforce on these tasks. Even so, the implementation of such kinds of robotic solutions raises technical challenges like force-based control or robot-to-human feedback. This paper presents a path-driven mobile co-manipulation architecture, proposing an algorithm that deals with all the steps of collaborative part transportation. Starting from the generation of force-based twist commands, continuing with the path management for the definition of safe and collaborative areas, and finishing with the feedback provided to the system users, the proposed approach allows creating collaborative lanes for the conveyance of large components. The implemented solution and performed tests show the suitability of the proposed architecture, allowing the creation of a functional robotic system able to assist operators transporting large parts on workshops.
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A REAL APPLICATION OF AN AUTONOMOUS INDUSTRIAL MOBILE MANIPULATOR IN INDUSTRIAL CONTEXT
Jose Luis Outón, Ibon Merino, Iván Villaverde, Aitor Ibarguren, Héctor Herrero, Paul Daelman, Basilio Sierra
2021
In modern industry, there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robots and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.
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OPERATOR – MOBILE ROBOT COLLABORATION FOR SYNCHRONIZED PART MOVEMENT
Aswin K Ramasubramanian, Nikolaos Papakostas
2021
Mobile robotic platforms have become increasingly popular. Commercially available versions of mobile robots are designed to support human operators in typical production environments. They may be used for transferring parts from one place to another, as well as for assisting the operator in a series of tasks, by utilizing the dexterity of their arm and end effector. This paper focuses on the development of a novel approach that allows the handling and transportation of parts through the simultaneous operation of human operators and mobile robots. In particular, a straightforward, easy to implement control strategy is used to adapt the operation of the mobile robot to the tasks carried out by the operator. This paper discusses also the advantages of introducing mobile robots in typical industrial environments and compares their potential against fully automated robotic solutions.
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ON USING HUMAN ACTIVITY RECOGNITION SENSORS TO IMPROVE THE PERFORMANCE OF COLLABORATIVE MOBILE MANIPULATORS; REVIEW AND OUTLOOK
Aswin K Ramasubramanian, Syed M. Aiman, Nikolaos Papakostas
2021
The operation of mobile manipulators in a collaborative environment needs to be adapted to the characteristics and skills of human operators. Human activity recognition, utilizing wearable sensors and vision systems, could be used to fine-tune the performance of the mobile manipulator so that human operators be better assisted. The goal is to develop a sense of safety and trust between the human and the manipulator in order to improve the ergonomics of the operator within the collaborative workspace. This paper reviews the technologies that can be used for activity tracking together with gait recognition as a biometric tool. These technologies could potentially allow the mobile robotic manipulator to dynamically adapt to the motion, skills, and intentions of the human operator and to the requirements of the task in action. This paper also proposes the idea of combining a gait recognition model and activity tracking towards improving the performance of mobile collaborative robots.
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CONTROL STRATEGIES FOR DUAL ARM CO-MANIPULATION OF FLEXIBLE OBJECTS IN INDUSTRIAL ENVIRONMENTS
Aitor Ibarguren, Paul Daelman, Miguel Prada
June 2020
The introduction of collaborative robots had a great impact in the development of robotic solutions for cooperative tasks typically performed by humans, especially in industrial environments where robots can act as assistants of operators. Even so, the coordinated manipulation of large and deformable parts between dual-arm robots and humans rises many technical challenges, ranging from the coordination of both robotic arms to the detection of the forces applied by the operator. This paper presents a novel control architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop. The obtained results demonstrate the validity of the implemented control architecture as well as its suitability for the implementation of collaborative cyber-physical systems.
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ONLINE PREDICTION FOR SAFE HUMAN-ROBOT COLLABORATION: A MODEL OF THE HUMAN ARM
Binchi Jacopo, Mangeruca Leonardo, Rucco Matteo, Orlando Ferrante, Minissale Alfio, Abba Fabio Francesco
2020
With the advent of new technologies and the transition of production to industry 4.0, a more flexible approach to manufacturing is pursued to achieve higher productivity. This transformation leads to overcoming traditional safety procedures and the development of new safety-assuring technologies for the minimization of risks connected with human-robot collaboration. In this work, we focus on the prediction of movements of operators’ upper torso and arms by developing a method which combines data-driven methodologies with formal methods. The approach is based on a predictive model of human motion compared against the planned robot trajectory and online monitoring of satisfaction of safety requirements with formal methods.
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PRELIMINARY DEVELOPMENT OF THE PSYCHOLOGICAL FACTORS ASSESSMENT FRAMEWORK
Eimontaite Iveta, Fletcher Sarah
17 April 2020
Robots, although not new in manufacturing, are still only just being directly integrated with human operators. Although timely and measured human factors integration in technology development can increase its acceptance, the impacts on manufacturing operators are still largely unknown. The proposed work described in this paper discusses the SHERLOCK project approach to human factors integration that aims to develop a standardised tool for evaluating the impacts of robotics in manufacturing. This analysis will enable the development of the framework, which will allow quicker assessment of psychological factors and recommendations for operator needs and requirements in a variety of manufacturing applications.
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AN APPROACH FOR MONITORING THE EXECUTION OF HUMAN-BASED ASSEMBLY OPERATIONS USING MACHINE LEARNING
George Andrianakos, Nikos Dimitropoulos, George Michalos, Sotirios Makris
18 February 2020
Sensing systems have been introduced safeguarding the operators, while primitive workflow monitoring systems, primarily based on operator’s feedback, enhance the dynamic behaviour of the system. This paper presents an approach to automatically monitor the execution of human-based assembly operations using vision sensors and machine learning techniques. A reference example based on the assembly of a water pump is showcasing the effectiveness of the proposed approach in real-life application.
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2D FEATURES-BASED DETECTOR AND DESCRIPTOR SELECTION SYSTEM FOR HIERARCHICAL RECOGNITION OF INDUSTRIAL PARTS
Ibon Merino, Jon Azpiazu, Anthony Remazeilles, Basilio Sierra
5 December 2019
Detection and description of key points from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for.
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BOUNDED COLLISION FORCE BY THE SOBOLEV NORM
Kevin Haninger, Dragoljub Surdilovic
12 August 2019
A robot making contact with an environment or human presents potential safety risks, including excessive collision force. Here, the Sobolev norm is adapted to be a system norm, giving rigorous bounds on the maximum force on a stiffness element in a general dynamic system, allowing the study of collision with more accurate models and feedback control.
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WHAT DYNAMICS SHOULD IMPEDANCE-CONTROLLED ROBOTS RENDER?
Kevin Haninger, Dragoljub Surdilovic, Arturo Bastidas Cruz
24 May 2019
While impedance control is the standard framework for physically interactive robots, the design choice of what dynamics should be rendered requires additional information (assumptions on environment, in-situ data). The range of dynamics which can be rendered by a robot is informed by its mechatronic design (actuators, physical compliance, inner loop control), and these mechanical design decisions must be made in advance. How can a mechatronic design be evaluated when the system objectives and environment dynamics are not quantified?
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