An approach to human behavior adaptable collaborative robot workplace design

Skill-based instruction of collaborative robots in industrial settings

With the advent of the Industry 4.0 era, the application of collaborative robots (cobots) in industrial fields has greatly increased. Intelligent collaborative robots or smart cobots enable high flexibility by combining the human ability to adapt to new tasks with automated robots (precision, repeatability) performance. This paper will analyze the current application status and usage of collaborative robots in the various industrial fields, and its benefits and disadvantages from the discussion. Firstly, this paper the defines collaborative robots and explains the difference between cobots and traditional industrial robots. Then, it analyzes the specific application cases of collaborative robots in automotive manufacturing, electronics and electrical appliances, food and beverage, pharmaceutical and chemical industries. Subsequently, this paper introduces the main technologies linked to collaborative robots, including sensors, Human-Robot Interaction (HRI), machine learning and safety technologies. Through the study of actual application cases, this paper summarizes the significant advantages of collaborative robots to improve productivity, reduce costs, and optimize the working environment. Meanwhile, this paper also discusses the current technical challenges, regulatory and standard issues as well as marketing difficulties for collaborative robots, and looks at future development trends and research directions. The results demonstrate that the utilization of collaborative robots in industry has a promising future and research and practical significance as well

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human-robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process.

In order to solve the consistency and integration about process and process data that is produced in digital product collaborative development, digital collaborative design process model is presented, by detailed analyzing the integration requirement of collaborative design process and data. Based on this model, the CAX/DFX tools which are used in collaborative design are distributed and granule encapsulated by Federal Intelligent Product EnviRonment(FIPER); collaborative design process data produced by CAX/DFX tools is encapsulated based on the logic of Product Data Management (PDM) system’s workflow and is published as Webservice. FIPER is used to realize the collaborative design process flow that uses data Webservice to data exchange between PDM and uses encapsulated CAX/DFX tools to realize the design function. An example which realizes the integration of process design flow of a part is presented at the end of this paper. The collaborative design process flow is used to organize the collaborative process planning and to encapsulate the collaborative design data with Webservice in this example. The realization of this example manifested that the digital collaborative design product model has good practicability and the method of encapsulation for CAX/DFX tools and data Webservice is feasibility.

Product design tends to be more complex and needs the effective cooperation among multidisciplinary designers and engineers. An ideal product design environment, which is both collaborative and intelligent, will be much appreciated. Agent technology is an innovative and promising approach to implementing distributed systems and is becoming popular in various areas of computing in recent years. In this paper, the dynamic nature of collaborative design process and the representation of collaborative design knowledge are presented. In order to model constantly evolving design process and the rationales resulted from collaboration, a Collaborative Product Data Model (CPDM), which extends traditional Product Data Model (PDM), and a constraint-based Collaborative Design Process Model (CDPM) are proposed. These are intended to facilitate the management and coordination of collaborative product design process and knowledge management. A design example is used to illustrate CDPM. While the system is being implemented, the architecture of our agent-based collaborative product design environment based on CPDM and CDPM is described.

Collaborative robots (Cobots) are changing the industrial scene rapidly by revolutionizing the mode of interaction between humans and machines. In contrast to traditional industrial robots, which worked in separate, fixed environments, Cobots work side by side with human employees to create safer, more flexible, and highly productive workspaces. In the last ten years, Cobots' integration has accelerated in industries such as manufacturing, healthcare, electronics, and logistics due to their flexibility, affordability, and capacity to learn from their surroundings. This review paper provides an in-depth analysis of the technological advancements, main challenges, real-world use, and future potential of Cobots. Based on insights from 25 top research papers, it explores how AI, machine learning, computer vision, and sophisticated sensors are being applied to turn Cobots more intelligent and intuitive. It also indicates the significant transition from automation systems based only on speed and repetition, to ones dedicated to collaboration, learning, and human-centered design. The methodology involves an extensive PRISMA-based literature review, interlacing empirical case studies, experimental studies, and theoretical debates to build a complete representation of the Cobot ecosystem. Apart from their operational benefits, this paper also discusses ethical issues, safety guidelines, and regulatory loopholes that need to be fulfilled to ensure large-scale adoption. Finally, the research indicates that Cobots are not only automating repetitive tasks but are empowering employees, making mass customization possible, and propelling industries into the next era of smart manufacturing and Industry 5.0. Nevertheless, issues such as cost constraints, technological limitations, and integration challenges are still obstacles that future studies need to break. Through the identification of existing trends, constraints, and areas of development, this review seeks to provide a reference point for researchers, engineers, and companies looking to unlock the full potential of collaborative robots.

[EMBARGOED UNTIL 8/1/2024] Order picking, which involves retrieving items from storage locations for an internal or external customer, is a core function in warehouses and accounts for up to 65 percent of the total operating cost. It is considered as a crucial driver for supply chain performance as improper planning of picking operations leads to inefficient asset utilization and delayed deliveries, which, in turn, adversely affects customer satisfaction, operating cost, and competitiveness. A majority of warehouses adopt a picker-to-parts system (workers travel through a warehouse to retrieve and transport items from storage to packing station), and the fulfillment speed of such order picking system (OPS) depends on the following key decisions - (i) set of orders to be picked in a tour (order batching decision), (ii) assignment of a batch to a picker and order in which their a processed (batch assignment and sequencing decisions), and (iii) route followed x by the picker to collect the orders in each batch (picker routing decision). However, with the growing customer demand and global labor shortage, warehouses are seeking efficient and less labor-intensive order picking systems. Autonomous mobile robots (AMRs) or collaborative robots (cobots) have the potential to alleviate the strain on human workers and expedite order picking operations. However, there are several key operational challenges to address for ensuring an effective collaborative human-robot order-picking system (CHR-OPS), where humans perform item retrieval tasks, and AMRs handle item transportation to the depot. This research aims to improve the fulfillment efficiency of a picker-to-parts CHROPS by optimizing key decisions associated with two warehouse picking strategies, namely, static picking and dynamic picking. In the case of static picking, the items to be retrieved from storage for a given day are known as apriority. On the other hand, the dynamic picking strategy allows for orders to arrive over time (e.g., e-commerce warehouses), and the pick cycle (or picking plan) can be updated in real time. First, we address the problem of optimizing the following key subproblems of a CHR-OPS with a static picking strategy: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts CHR-OPS system employing multiple pickers and AMRs. A mixed integer linear programming model is developed to jointly optimize the three subproblems with the objective of minimizing the total tardiness of all orders. The MILP model is validated and solved to optimality for small instances. However, since the problem under consideration is NP-hard, it is computationally intractable for larger instances. To efficiently handle large instances, we proposed deterministic and stochastic local search algorithms, namely variable neighborhood descent and a new variant of simulated annealing. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human{robot team composition, AMR speed, AMR capacity and warehouse layout. Subsequently, we address the CHR-OPS with dynamic picking and developing interventionist picking algorithms for a multi-robot multi-human setting. Specifically, we propose two interventionist policies for dynamic collaborative order picking, namely, the collaborative human-robot interventionist picking algorithm (CHRIPA) and the collaborative human-robot rule-based interventionist picking algorithm (CHR-RIPA). The evaluation of the policies demonstrates that the proposed rules can improve the overall performance of the system compared to benchmark approaches (human-only dynamic picking and collaborative picking with no intervention). In addition, results indicate CHR-IPA outperforms CHR-RIPA in terms of average tar diness and order completion time, albeit with a slightly higher travel distance for human workers. The results have led to several managerial implications for a collaborative order picking system. Further, the proposed models and algorithms are modular and can be adapted to any warehouse setting by accounting for the relevant parameters such as warehouse layout, AMR capacity and human-robot composition. Finally, the directions for future research have been identified and summarized.

Nowadays, the current market trend is oriented toward increasing mass customization, meaning that modern production systems have to be able to be flexible but also highly productive. This is due to the fact that we are still living in the so-called Industry 4.0, with its cornerstone of high-productivity systems. However, there is also a migration toward Industry 5.0 that includes the human-centered design of the workplace as one of its principles. This means that the operators have to be put in the center of the design techniques in order to maximize their wellness. Among the wide set of new technologies, collaborative robots (cobots) represent one such technology that modern production systems are trying to integrate, because of their characteristic of working directly with the human operators, allowing for a mix of the flexibility of the manual systems with the productivity of the automated ones. This paper focuses on the impact that these technologies have on different levels within a production plant and on the improvement of the collaborative experience. At the workstation level, the control methodologies are investigated and developed: technologies such as computer vision and augmented reality can be applied to aid and guide the activities of the cobot, in order to obtain the following results. The first is an increase in the overall productivity generated by the reduction of idle times and safety stops and the minimization of the effort required to the operator during the work. This can be achieved through a multiobjective task allocation which aims to simultaneoulsy minimize the makespan, for productivity requirements, and the operator’s energy expenditure and mental workload, for wellness requirements. The second is a safe, human-centered, workspace in which collisions can be avoided in real time. This can be achieved by using real-time multicamera systems and skeleton tracking to constantly know where the operator is in the work cell. The system will offer the possibility of directing feedback based on the discrepancies between the physical world and the virtual models in order to dynamically reallocate the tasks to the resources if the requirements are not satisfied anymore. This allows the application of the technology to sectors that require constant process control, improving also the human–robot interaction: the human operator and the cobot are not merely two single resources working in the same cell, but they can achieve a real human–robot collaboration. In this paper, a framework is preented that allows us to reach the different aforementioned goals.

Collaborative robots are an emerging technology falling within the scope of Industry 4.0 and based on the concept of Human‐Robot Collaboration (HRC). Unlike traditional industrial robots, collaborative robots are used in shared workspaces with no safety fences. Hence, prospective hazardous contacts need to be avoided or mitigated through a risk assessment. The normative standards such as ISO TS 15066 suggest a list of common hazards, but do not guide the robot system user through the risk assessment process. To address this shortcoming, this paper proposes a practical eight steps risk assessment approach, resulting in a risk priority list. In order to provide an accurate, practical, quantitative and supportive tool for HRC environments, the Failure Mode and Effects Analysis (FMEA) and the Proportional Risk Assessment technique (PRAT) techniques are proposed for risk assessment. The two techniques mentioned above are combined in the suggested new methodology, highlighting both their benefits and disadvantages. The proposed methodology is applied with positive results to a collaborative brick‐lifter case study.

Collaborative robots are progressively employed to support human workers in assembly tasks or independently assigned to one station to complete assembly tasks in U-shaped assembly lines. This study develops two exact methods, based on constraint programming (CP) model and Combinatorial Benders’ decomposition algorithm (CBDA), to tackle the U-shaped assembly line balancing problem with collaborative robots. The CBDA is based on distinct decomposition strategies that address the master problem and the subproblem, formulated by a mixed integer linear programming (MILP) model and a CP model, which is designed to obtain a real objective value and generate cuts that refine the master problem. Additionally, MILP-based and CP-based local search techniques are proposed to improve the algorithm's performance. The computational study demonstrates that both the proposed methods, namely CP and CBDA, outperform the current state-of-the-art methods (the MILP model and simulated annealing algorithm). Furthermore, the CP outperforms the CBDA in terms of the number of optimal solutions, achieving the optimal solutions for 574 out of 1500 test instances, and might be regarded as the new state-of-the-art methodology.

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.

BACKGROUND: The economic component of the effective functioning of technological machines consists in their uninterrupted operation when performing various tasks in such industries as construction, road, agriculture, etc. It is currently possible to take into account the variety of operating conditions of technological machines and constantly changing loading modes by using digital technologies that allow creating an array of databases not only in real time, but also for an individual machine. A relevant task is to create methods for collecting information about the operating modes of the machine, the factors causing changes in the efficiency of operation, the development of algorithms for making decisions about maintaining the working condition of all units and systems of the machine, preventing their failures and non-production downtime of the machine as a whole. OBJECTIVE: Ensuring the effective functioning of a single technological machine by managing the risks of failures, by adjusting the frequency of maintenance and periods of repair-and-restoration activities based on the real-time data on the technical condition and digital processing of decision-making information. METHODS: The nature of changes in the technical condition of units and systems of technological machines in the theory of systems is most often considered as random due to the high probability of uncertainty of factorial influence. It is proposed to consider the problem of failure risk management of system elements and units of technological machines using the basic provisions of the excursion theory. The object of the study is a diesel engine of a technological machine with an example of monitoring the technical condition of the fuel system. RESULTS: The justification of the expediency of performing maintenance and repair-and-restoration activities (MaR) of technological machines as required is presented. The adjustment of the MaR frequency was carried out based on the data of changes in the performance of the machine, in particular the internal combustion engine fuel system. To develop an algorithm for the formation of a data array, typical architectures for collecting and processing information using digital platforms for decision-making on the intensity of parameter changes are used, as an example, oscillograms of pressure changes during the operation of the internal combustion engine fuel system are presented. CONCLUSIONS: A module for diagnosing the technical condition and efficiency of the internal combustion engine of a single technological machine in real time has been developed. It is proposed to introduce an intelligent decision-making system with subsequent transformation into the form of a digital twin of failure risk management and control of the efficiency of the machine for various operating conditions.

The use of collaborative robots (cobots) and human robot collaboration has started to increase in diverse industrial areas in the past years. However, cobots are seldom applied in construction work even if there is a growing need for robotics solutions in this area. This study aims to present the possibilities of applying lightweight cobots for single tasks in the construction sector. This paper presents the development of a proof-of-concept dedicated to screwing gypsum board panels to the ceiling of a room. After identifying the challenges related to this task, we present the cobot solution that we implemented for this purpose. Our results show that repetitive screwing work in upright position achieved by cobot can benefit workers by removing ergonomical strain. At the same time, cobot ensures that this task is achieved with constant performance. Future research directions lead to the search for better accuracy in screw positioning and more dynamic ways of collaboration with human workers.

Collaborative robots for industrial tasks: A review

The steep rise of interest in collaborative robots applied to industrial tasks is fallout from the emergence of Industry 4.0 in most industrialized countries. Workcells with both human workers and robots employed together extend the field of use of robotics to medium and small series productions. In a collaborative workplace, robots and humans can exploit their mutual skills and strengths to the best effect. With the goal of supporting collaborative robotics, this chapter points out several issues that may arise by introducing robots in manual production. This chapter’s statement is that the success of collaborative workcells in manufacturing relies on dedicated and accurate design activities and should not be approached by the mere addition of a robot to the existing workplace.