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For further increased flexibility of high variant manufacturing, deployment of collaborative robots can be an economical proposition. Of particular present relevance is collaborative small parts assembly in a mixed environment with human workers and with robots operating according to the protective paradigm of power and force limiting. Safety legis...
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Citations
... It characterizes the types of human-robot contact, whether quasistatic or transient. Furthermore, maximum values for biomechanical load limits are given to prevent injury or damage when the robot collides with a different body as well as additional protective measures [54]. A maximum speed limit is set based on the moving mass of the robot and the body part impacted. ...
The human-centric approach is a leading trend for future production processes, and collaborative robotics are key to its realization. This article addresses the challenge of designing a new custom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard” safety and motion control technologies. This is conducted on a non-conventional cable-driven parallel robot (CDPR). Safety is assured by safe commands to individual axes, known as safe motion monitoring functionalities, which limit the axis’s speed in the event of human intrusion. At the same time, the robot’s motion controller applies an override to the toolpath speed to accommodate the robot’s path speed to the limitations of the axes. The implementation of a new Pre-Warning Zone prevents unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.
... Until 2016, it was possible to adhere to a single chapter inČSN EN ISO 10218 that addressed the possibilities of collaboration between robots and humans; however, the definition was insufficient. Therefore, a technical specification, ISO/TS 15066:2016, was issued specifically for collaborative robots [3,4]. Human-robot collaboration places high demands on safety, as the human shares an asymmetric workspace with the robot without protective fencing. ...
This article discusses creating a methodology for the asymmetric measuring of values and processes of collision forces and pressures of the collaborative robot dependent on time. Furthermore, it verifies the usefulness of this methodology in practice by successfully performing the experimental measurement and verifying the possibility of using these results by analysing and stating the collaboration level for a robot of the given type. According to the suggested methodology, the measurement results are a specific output based on real measured data, which can be easily rated and can quickly determine the collaborative level of any robot. Measurements were aimed at determining the values of pressure and force with which the robot acts at certain speeds related to distance from the base. Due to the controlled symmetrical impact of the robot on the measuring device, the transfer of energy from the robot to the human body was guaranteed. In theoretical terms, this article primarily provides the assembly of the theoretical foundation of the collaborative environment between humans and robots, and a comprehensive overview of the possibilities of using the technical specification ISO/TS 15066:2016 when deploying a robot in collaboration with humans in a collaborative environment. This new information is highly valuable for both manufacturers and users of collaborative robots. The presented article analyses the possibilities of measuring collaboration and safety elements in cooperation with a robot. The most significant practical benefit is the presentation of a methodology for measuring robot collaboration and verifying its functionality by conducting experimental measurements of robot collaboration according to this methodology. The measurement was performed on a robot made by Universal Robots, model UR10. The measurement coordinates were stationed in a way to create a spatial measurement model. Boundary coordinates of the spatial model were as follows: [450; 200], [450; 500], [850; 200], and [850; 500]. Collisions were measured at 8 different speeds for each coordinate (20 mms−1, 50 mms−1, 100 mms−1, 200 mms−1, 250 mms−1, 300 mms−1, 350 mms−1, and 400 mms−1) to enable the observation of changes in accordance with speed. The measured values indicate a significant fact: the closer the collision is to the robot’s base, the higher the collision forces. An important aspect is that the measured values were only for speeds up to 400 mms−1, which is a very low value for industrial use to meet the desired cycle time. It can be stated with absolute certainty that speed has the greatest impact on collision force values. The speed of the collaborative robot arm can therefore be considered a limiting factor for use in industrial applications with a requirement of a short cycle time. Focusing on the results of the measured values, it can be stated that a new finding is the correct design of robotic movements in relation to possible contact with humans is crucial. The result of the measurement according to the proposed methodology is a specific output of realistically measured data, which can be easily evaluated and the level of collaboration of any robot can be quickly determined. The measured data will also serve as a basis for further processing and preparation of new simulation software. It will be possible to use the intended software for detecting and predetermining the safe asymmetric movements of the collaborative robot already at the stage of production preparations, unlike the method of measuring force and pressure on robots which can be used until the time of implementation into production. In the future, this software may also allow users of collaborative robots to easily and quickly evaluate the robots specified.
... It characterizes the types of human-robot contact, whether quasi-static or transient. Furthermore, maximum values for biomechanical load limits are given to prevent injury or damage when the robot collides with a different body, as well as additional protective measures [52]. A maximum speed limit is set based on the moving mass of the robot and the body part impacted. ...
The human-centric approach is a leading trend for future production processes, and collaborative roboticists are key to its realization. This article addresses the challenge of designing a new cus-tom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard" safety and motion control technologies. This is done on a non-conventional Cable-Driven Parallel Robot (CDPR). Safety is assured by safe commands to individual axes, known as Safe Motion Monitoring Functionalities, which limits the axes speed in the event of human intrusion. At the same time, the robot's Motion Controller applies an override to the toolpath speed to accommodate the robot's path speed to the axis’s limitations. The implementation of a new Pre-Warning Zone, prevent unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.
... Since HRC enables a collaborative robot and its human partner to collaborate closely in a shared workspace, Type A standards and Type B standards, which are specified for industrial robots and machinery, are not sufficient in supporting HRC and collaborative robots. Instead, Type C standards are the most relevant and authoritative international standards for collaborative robots, such as ISO/TS 15066 (Matthias et al., 2016). Safety-related guidelines and instructions in ISO/TS 15066 include the following four primary modes: ...
Human–robot collaboration (HRC) plays a pivotal role in today’s industry by supporting increasingly customised product development. Via HRC, the strengths of humans and robots can be combined to facilitate collaborative jobs within common workplaces to achieve specific industrial goals. Given the significance of safety assurance in HRC, in this survey paper, an update on standards and implementation approaches presented in the latest literature is given to reflect the state-of-the-art of this prominent research topic. First, an overview of safety standards for industrial robots, collaborative robots, and HRC is provided. Then, a survey of various approaches to HRC safety is conducted from two main perspectives, i.e., pre-collision and post-collision, which are further detailed in the aspects of sensing, prediction, learning, planning/replanning, and compliance control. Major characteristics, pros, cons, and applicability of the approaches are analysed. Finally, challenging issues and prospects for the future development of HRC safety are highlighted to provide recommendations for relevant stakeholders to consider when designing HRC-enabled industrial systems.
... A large number of parameters have to be taken into account when implementing PFL. They concern data linked to the robot, such as its weight, the weight of the useful load, the weight of the robot elements in movement, and the information linked to the application like the regions of the body exposed, the surface area and shape of the contact area and the type of contact: quasi-static or transient [11]. This diversity of parameters can make the task of configuring the robot quite complex, given that the impact of each of them on the global behavior of the PFL function is not clearly detailed. ...
... Most of the industrial robots' controllers including the UR, are closed architecture (i.e., the control designer has no access to the lowlevel control). Furthermore, existing standards [24] and methods of human-robot collaborative systems are focused on safety concerns, such as obstacle avoidance [25]. To introduce human skills and experience into the robotic system, the collaboration between human and robots should not only remain on the obstacle avoidance, but also be extended to response human input in the real time with satisfactory control performance. ...
Human skill-based robotic control to perform critical manufacturing operations (e.g., repair and inspection for high-value assets) can reduce scrap rates and increase overall profitability in the industrial community. In this study, a human–robotic collaborative control system is developed for accurate path tracking subject to unknown external disturbances and multiple physical constraints. This is achieved by designing a model predictive control with a sliding-mode disturbance rejection term. To rule out the possibility of the constraints violation caused by external disturbances, tightened constraints are formulated to generate the control input signal. The proposed controller drives the robotic system remotely with enhanced smoothness and real-time human modification on the outputted performance so that the human experience can be fully transferred to robotic systems. The efficacy of the proposed collaborative control system is verified by both Monte–Carlo simulation with 200 cases and experimental results including tungsten inert gas welding based on a universal robot 5e with 6 degree-of-freedom.
... The cloud provides infinite processing capacity for the reception, storage and analysis of big data necessary for optimum system operations. Stored information and services may be accessed through the Internet at any location and device [15]. It enables all system parts to coordinate their work and concurrently work in real time on sharing data and information. ...
The needs of the business are evolving at a fast pace, and companies are rolling out and investing in innovative solutions, new products/services, new business models to stay ahead in market competitions. At the consumer level, the demands of the individualism of products are increasing. The new‐age customers want to customize their products, make suggestions for product innovation and express interest in taking part in the development processes. The fluctuations in the demands of the volatile market are one of the main drivers of paradigm shifts in manufacturing processes as this requires fast and flexible adaptability. By an estimate, the demands will double by 2050. By this estimate, it makes clear that there is a need for a sustainable and energy‐efficient manufacturing system. This is where Industry 4.0 comes into play. In the past decades, there has been steady development in the field of information technology in general. These developments in a way have brought a revolution in the way we live and how the business/industries are performed. This new age Industrial Revolution paves way for intelligent manufacturing systems that are flexible, adaptive, energy‐efficient, and innovative. This revolution involves various technologies, such as wireless networks, data analysis, AI, human‐machine interaction, and 3D Printing. However, in addition to these opportunities and transformations, there lie challenges, too. Integration of data and analyzing them efficiently, floor level process flexibility, and security of the systems are some of the main challenges to the smart systems. However, fortunately, the pace at which mankind has been able to resolve problems and bring out solutions gives hope that these smart systems are not far from reality.
... Vision-based security measures have the advantages of strong adaptability and high intelligence, and have become a hotspot in the field of robot security in recent years [2].After international cooperative robot-related specifications are released, much research is concentrated in the field of collaborative robot safety protection. The standard of human-robot collaboration is defined in ISO/TS 15066 [3]. Collaborative operations may include one or more of the following methods: safety-rated monitored stop, hand guiding, speed and separation monitoring, and power and force limiting. ...
Human-robot collaboration (HRC) based on speed and separation monitoring should consider the difference of risk factors in the scene; otherwise, the sudden invasion of non-operators or routine operation of the operator may stop the robot system. In this paper, we propose a sensing network based on the fusion of multi-information to obtain scene semantic information and employ it to realize risk assessment. However, due to the influence of light on the image information sensed by RGB cameras, it is not easy to obtain accurate scene semantic information. We apply a depth camera and a thermal imager to obtain depth and infrared information to enhance the RGB images. We build a risk information database and use it to quantify the obtained scene semantic information into risk factors. The dynamic change of risk factors judges whether the distance between humans and robots is safe. The experimental results verify that the algorithm of intelligent human-robot monitoring can realize the analysis of dangerous situations and control the robot system, thereby reducing the number of false shutdowns and improving safety.
... Safety is typically handled at the robot itself using dedicated safety sensors and computing hardware. However, to enable the increasing demand for fl exibility, new dynamic safety concepts are required [7], which need additional compute. Hence, safety-related workloads may also need to be off loaded to the Edge server with latency awareness and time supervision. ...
... In future systems, the safety layer will be required to perform more complex calculations to achieve the goals defi ned; for example, in the ISO TS15066 standard [7], which pertains to more elegant handling of dangerous situations than safety stops. To achieve this, comprehensive 3D environment models with object positions, heading, speed and behavior are required which add significant compute demand. ...
Mobile multi-robot systems are an integral component of highly automated factories of the future. Since mobile robots have limited on-board computing capability and battery capacity, there is increasing interest in exploring approaches that enable robots to effectively leverage wireless communications and Edge Computing solutions for perception, navigation, planning, coordination, and control. It is, however, a major challenge achieving precision, high-speed, co-ordinated actions between robots due to tight end-to-end latency, and safety requirements, especially while enabling time-sensitive data exchange over wireless networks and execution of computing workloads distributed across robots and the Edge system. The traditional approach of designing compute, communications, and control components in an Edge system as independent components, limits the capacity and scalability of computing and wireless resources and is therefore unsuitable to meet performance guarantees for energy and resource-efficient time-sensitive robotic applications. In this article, we discuss technical challenges in the context of two Edge Robotics use cases such as conveyer object pick-up and robot navigation, which are representative of time-critical control in IoT applications. We propose research directions grounded in an end-to-end system co-design paradigm and describe technology components such as virtualized robot functions, compute-communications-control co-design, Edge system co-simulation, safety and security aspects that are core to Edge Robotics. We also briefly outline future research directions that are necessary to pave the path toward factory-scale Edge Robotics systems.
... Here, Fmax is the maximum contact force for a specific human body region, Pmax is the maximum contact pressure for a specific body area, k is the effective spring constant for a specific body region, µ is the reduced mass of the two-body system that considers mH as the effective mass of the human body region, and mR as the effective mass of the robot as a function of robot posture and motion. An example of the implementation of the PFL in collaborative applications can be found in [61]. ...
The reduction of physical barriers between humans and robots has been accelerated in recent years by advancements in
industrial robotics and sensor technologies. To ensure safety in human-robot interaction (HRI), various collaborative
operations can be implemented according to recent standards and deliverables. This paper presents a review of recent
research and progress on HRI, related to the available collaborative operations with emphasis on human safety. The
current state of research and technology regarding speed and separation monitoring, power and force limiting as well as
the combination of both collaborative operations is analysed in detail.