Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

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.

Current paper deals with a new tendency in robotics, consisting in broader and broader exploiting of socalled collaborative robots, sharing their workspace with humans. An overview of research in this area has been performed. Some aspects of robot programming method, called programming by demonstration, have been discussed. In particular, the authors try to answer the question, whether the idea of direct human-robot cooperation can be applied also in welding. Examples of research in this area have been presented. The paper discusses some as- pects of human-robot communication. It presents a conception of a novel method of calibration of human-robot voice communication system, now under development at the Warsaw University of Technology. This method is focused on specific requirements resulting from the use of collaborative robots.

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health

This article presents a disassembly task planning algorithm considering human–robot collaboration (HRC) and human behavior prediction (HBP). Unlike assembly procedures, the disassembly of end-of-life (EOL) products has been a labor-intensive process with uncertainties difficult to cope with. Meanwhile, it is usually challenging to obtain an optimal sequence efficiently without excessive computational cost. Also, the conventional human-centered task planning, in which the robot has to halt frequently due to unsafe interruptions by human motions, may decrease the efficiency of the disassembly process. In this article, a sequence planner is proposed to assign tasks in real time between a human operator and a robot to overcome the aforementioned challenges. The cost function includes the effort of the human and the robot in terms of both movement distance and time spent on the tasks. The constraints include the disassembly rules and the safety of the human operation. The optimal sequence is generated by solving an optimization problem in a receding-horizon way. In particular, at each step, the proposed disassembly sequence planner locates the workers (a human operator and a robot) and the to-be-disassembled components, predicts human movement for the next several steps, and obtains the optimal disassembly sequence for the next several steps following disassembly rules and safety constraints. Experiments have been extensively conducted on the disassembly of a wooden toybox and a used hard disk drive (HDD) to validate the proposed disassembly sequence planner. The planner has successfully generated the disassembly sequence in an HRC setting explicitly considering real-time human motion prediction and assigned the human operator and the robot to collaboratively complete disassembly tasks without violating disassembly rules and safety constraints.

Towards a circular economy for electrical products: A systematic literature review and research agenda for automated recycling

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation.

The evolution of industrial robotics has advanced from isolated, caged systems through basic human–robot interaction (HRI) to sophisticated human–robot collaboration (HRC). However, conventional vision systems based on red, green, blue (RGB) cameras remain a significant limiting factor in realizing the full potential of collaborative automation. This comprehensive review examines the transformative role of event cameras in advancing HRC capabilities and addressing current limitations in industrial settings. Event cameras, with their microsecond-level temporal resolution and robust performance under challenging lighting conditions, offer substantial advantages over traditional RGB cameras that are constrained by fixed frame rates and ambient lighting dependencies. We present a systematic framework for leveraging event cameras to enhance the human state understanding in collaborative robotics, encompassing real-time detection of poses, gestures, facial expressions, and emotional states. This framework addresses fundamental challenges in workplace safety and collaborative efficiency while enabling more sophisticated and responsive HRC systems. Our review synthesizes recent research developments in event camera applications specific to HRC, providing a detailed comparative analysis of their advantages over conventional vision systems. We identify emerging opportunities and potential research directions for advancing event-based vision in industrial robotics. In addition, we examine integration challenges and propose strategies for implementing event camera technology in existing industrial infrastructure. This work contributes valuable insights into the future trajectory of adaptive and intuitive HRC systems, offering a roadmap for researchers and practitioners in the field of industrial automation.

The voltage support functions have been mandated in grid codes to help wind turbines (WTs) ride through AC faults. In a modular multilevel converter-based high voltage DC (MMCHVDC) system for wind farm integration, particularly in scenarios where there is no conventional synchronous generator at the sending end, the MMC adopts voltage-frequency control to form an AC grid. Such grid, however, is found to have a distinctive weak characteristic, which significantly alters the relationship between the point of common coupling voltage and the current injected by WTs and makes existing voltage support modeling methods and control strategies for grid-connected inverters inapplicable to the WTs connected to MMC-HVDC. To enhance the voltage support performance for the WTs, this paper aims to elaborate on the weak grid characteristic of the voltage formed by the MMC. To streamline the analysis, the concept of generalized fault impedance is first proposed to eliminate the need for numerous models built for scenarios with different network structures, negative sequence controls, fault types, and transformer winding configurations. Additionally, this paper categorizes the operating modes of the MMC and utilizes the piecewise modeling method to derive precise control equations in each operating mode, based on which the weak grid characteristic is thoroughly discussed. Subsequently, to avoid globally identifying the optimal voltage support point, an operating mode selection method prior to the voltage support realization is proposed. Finally, the above modeling method and analysis are verified through control-hardware-in-the-loop experiments.

Full-duplex (FD) radio with wireless backhauling, is a key technology for dense small cell deployments in fifth generation (5G) cellular networks. In this paper, the problem of joint user scheduling, operation mode selection and power allocation in a two-tier cellular network with FD-capable small basestations is considered. The objective is to maximize the network sum rate under quality-of-service requirements and backhaul capacity constraints. In order to solve the problem, a distributed algorithm is developed which divides the original problem into two subproblems, viz., (i) user scheduling and mode selection; and (ii) power allocation. For the first subproblem, a low-complexity search method is proposed for opportunistically finding the best user scheduling and operation modes. In the second subproblem, an iterative successive convex approximation method is proposed to transform the nonconvex power allocation problem into a sequence of convex subproblems. The proposed solution provides a hybrid strategy which opportunistically selects FD or half-duplex (HD) operation modes for access and backhaul links. It is observed that the proposed algorithm converges in few iterations and mitigates the interferences by properly adjusting the transmission powers. Numerical results demonstrate the superiority of the proposed scheme over other possible scenarios. In particular, the proposed algorithm can achieve up to 44% gain in overall sum rate compared to a network that solely uses HD.

The recycling of discarded products is an integral part of resource utilization. A disassembly line balancing problem (DLBP) concerns the recycling and remanufacturing process of end-of-life (EOL) products. Disassembly time is affected by many factors, e.g., the precedence relationships among disassembly tasks, and the skills and learning abilities of disassembly workers. In this paper, we use Petri nets to specify a disassembly process and establish a mixed integer programming model to describe DLBP that considers the learning effect of human workers. We then propose a discrete fruit fly optimization algorithm to solve the proposed problem. By comparing its experimental results with other intelligent optimization algorithms’, its efficiency is verified.

Human-robot collaborative disassembly line balancing considering the safe strategy in remanufacturing

The circular economy follows the principle of reducing resource usage and energy consumption, reusing usable resources including subassemblies and components in discarded or used products, and recycling usable materials. It is guided by saving resources, improving the utilization rate of resources, reducing pollution, and protecting an ecological environment. Effective product disassembly planning methods can improve recovery efficiency and promote the circular economy. However, the existing studies pay little attention to sequential dependency disassembly, which makes it difficult to implement the existing planning methods under the constraints of limited disassembly methods and tools. In this paper, a single-objective sequence-dependent disassembly line balancing problem (SDLB) is studied. This problem requires that disassembly tasks are assigned to a group of orderly disassembly workstations to obtain the near optimal solution while meeting a disassembly priority constraint. Because solution complexity increases with the number of parts in a product, an improved artificial bee colony method (IABC) is proposed to solve the problem. Through experiments and compared with a genetic algorithm, the effectiveness of the proposed algorithm is verified.

Daily activities have been increasingly supported by intelligent devices and applications. Smart devices are constantly communicating through the Internet of Things (IoT) networks, either by sending collected data and notifying the actions taken or by receiving instructions for actions to be taken. Most of this communication requires the confidentiality of data through the usage of encryption algorithms, being the Advanced Encryption Standard (AES) algorithm one of the most used. However, how do the operation modes of AES algorithm perform in a resource-constraint device? This paper aims to evaluate the impact on the time to encrypt and decrypt different sized messages in IoT devices when using each one of the five AES modes of operation and the three key sizes defined. The test scenario was implemented using two programming languages, running on a Raspberry Pi device. The results achieved infers that Python was quicker and had a more homogeneous result set than JavaScript implementation in most AES operation modes. These results help to understand the trade-off between IoT devices' security needs and delays in communication caused by the selection of the AES algorithm operation mode.

The topic of this thesis is design for recycling (DfR) of electronic products. More than ever before, electronic products are intruding into our everyday life, both in the household and in industry, resulting in increasing numbers of electronic products ending up in waste streams (also known as electronic waste, or e-waste). Electronic products contain a wide range of materials, such as iron, steel, lead, plastics, glass, aluminium, copper and precious metals. Materials are assembled using different types of connections, such as click or snap-fit joints, adhesive tape, screws, glue and soldering. Furthermore, different arrangements and positioning of materials, as well as the connections between them, lead to different kinds of product structures. These characteristics can make the recycling process for electronic products more or less difficult. Design is seen as pivotal in the creation of products that can facilitate the recycling process. For this reason, in the past two decades there has been considerable research on DfR, resulting in a large number of methods and tools being developed. The aim of these methods is to assist designers in assessing the recyclability of their designs and to select adequate product design features that facilitate the recycling process. However, these methods do not seem to have been very effective; particularly not in the case of electronic products. This is because, despite the considerable number of methods developed thus far, and what they claim in theory, electronic products are still not being optimally disintegrated and separated in actual recycling processes. Consequently, the aim of this thesis is to uncover the various reasons for the mismatch between the theory and practice of DfR by undertaking a number of studies.