Collaborative Robots Research Articles

Optimizing Communication for a Humanoid Subs-Talker Robot Using RSSI Analysis on ROS System

This study aims to optimize communication for the humanoid "Subs-Talker" robot using Receive Signal Strength Indicator (RSSI) analysis within the Robot Operating System (ROS). The background of this research highlights the importance of stable and strong inter-robot communication in the Indonesian Humanoid Robot Soccer Contest (KRSBI-H), where robots need to function in synergy. The method involves measuring signal strength at specific distances, both with and without the use of an extender device, utilizing the InSSIDer application for RSSI data recording and analysis. The results show that using an extender improves communication stability, especially at longer distances (7.2 and 9.0 meters), although the impact is minimal at closer distances. The discussion suggests that an extender can be an effective solution for enhancing inter-robot communication quality in competitions. In conclusion, the extender significantly improves the communication range and signal quality among robots, supporting optimal performance in collaborative robot interactions within the competition arena.

Application of the Water-Based Electro-Hydraulic Actuator (EHA) to the Heavy-Duty Collaborative Robot

In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was employed to relate the rotational angles and the end-effector’s location, facilitating the design of the actuators. The maximum required torques for joints 2 and 3, responsible for lifting for 12 s, were calculated under quasi-static and dynamic loading conditions. The results showed that the maximum required torques were 126.67 Nm and 58.86 Nm for joint 2 and 3, respectively. The maximum torque for joint 2 occurs when the pitch links are fully extended, whereas the maximum torque for joint 3 occurs when the third link is parallel to the ground. The torques, due to the inertia and Coriolis dynamic terms, were also calculated and found to be lower than those required for the gravitational term. Various maneuvering scenarios, along with Ansys Motion simulation, were analyzed for the verification of the results. Based on the calculated maximum torques, the linear actuators of the EHA were designed. The heavy-duty cobot can be built with the developed actuator proposed in this paper. The total weight of the entire frame was measured to be 14.59 kg, resulting in a high Payload/Weight (P/W) ratio of 1.37. In conclusion, the robot was made lighter and can operate more efficiently, particularly for heavy loads up to 20 kg.

Task Allocation and Sequence Planning for Human–Robot Collaborative Disassembly of End-of-Life Products Using the Bees Algorithm

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions.

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.

Wrap‐Around String‐Like ToF and Self‐Capacitance Combined Sensor for Robots

Collaborative robots are becoming popular because they can operate without safety fences and can work in close proximity to humans. Proximity and tactile sensors on the robot's surface are important safety measures for existing robots. In this paper, we propose a string‐like ToF and self‐capacitance combined sensor that can be easily wrapped around various types of existing robots. The sensor can be freely connected to other sensors, and the length of the connected sensors attached can be adjusted depending on the shape of the robot. Prototype sensors were fabricated, and four connected sensors were wrapped around the robotic arm. Sensors on the robot's surface can detect objects within the proximity range and determine contact. In addition, a robotic arm with sensors can be controlled in real‐time using measured data from the sensors. It was confirmed that the proposed sensor can be used for proximity and tactile sensing on existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration

Energy-efficient human–robot collaboration poses significant challenges to the sustainable operation of production systems. Therefore, our work proposes novel robotic edge intelligence to address the issue. First, robotic edge intelligence is proposed to fully utilize the embedded computing capabilities of edge robots, and the state transition diagrams are developed for jobs, humans, and robots, respectively. Second, a multi-objective model is designed for the energy-efficient human–robot scheduling problem to evaluate the production performance and energy efficiency as a whole. Third, a heuristic algorithm is developed to search for the optimal solutions based on an artificial plant community, which is lightweight enough to be run on edge robots. Finally, a benchmark data set is developed, and a series of benchmark experiments are implemented to test the proposed system. The results demonstrate that the proposed method can effectively enhance energy efficiency and production performance with satisfying solution performance.

The Analysis of Applied Innovative Mechatronics Technology in Intelligent Driving - A Case Study of New-Energy Vehicles' Development in Mainland China

Abstract. The interdisciplinary field of mechatronics, which amalgamates elements of mechanical, electrical, computer, and control engineering, has fundamentally transformed the automotive industry. This transformative influence is evident in the development of advanced vehicular functionalities such as Automatic Fuel Filling Systems, Car Attitude Control, Antilock Brake Systems (ABS), and Electronic Stability Programs (ESP), as well as in the advent of vehicles equipped with semi-active suspensions. The integration of mechatronics has not only enhanced vehicle safety, efficiency, and performance but has also positioned itself as a cornerstone for the burgeoning electric vehicle (EV) market, particularly in China. Chinese companies, leveraging extensive smart factory and automation technologies, have consequently secured a dominant stance in the global EV landscape, capturing a staggering 60% market share. This paper explores the multifaceted applications of mechatronics in the automobile industry of industrialized nations, with a special focus on its pivotal role in China's technological ascendancy in automotive manufacturing. Through an exploration of practical applications including IoT integration and collaborative robotics, this study underscores the transformative potential of mechatronics in redefining automotive production and operation, propelling the industry toward unprecedented levels of innovation and efficiency.

Integration of Artificial Vision and Image Processing into a Pick and Place Collaborative Robotic System

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified.

Thermal environment simulation and product visual design of flexible manufacturing system based on robot collaboration

Thermal environment simulation and product visual design of flexible manufacturing system based on robot collaboration

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

Autonomous ecologies of construction: Collaborative modular robotic material eco-systems with deep multi-agent reinforcement learning

To address the unprecedented challenges of construction pressurized by the global climate crisis, housing shortage, and growing shortage of skilled labor, this research presents a radical shift in the construction lifecycle of buildings, from linear processes that produce static continuous buildings to interrelational processes linking adaptative eco-systems of collaborative robots and reconfigurable building parts. Inspired by natural builders, the interdisciplinary field of collective robotic construction (CRC) offers the potential for scalable, adaptive, and resilient construction with simple robots. We establish a design framework for autonomous collaborative robotic construction (ACRC) through modular robotic material eco-systems (MRMES) trained with deep multi-agent reinforcement learning (DMARL). This involves the integration of three core aspects: (1) modular robotic material eco-systems (2) cyber-physical simulation and control with bidirectional feedback (3) adaptive intelligence through deep multi-agent reinforcement learning. The framework is implemented through three comparable case studies for collaborative modular robotic assembly of reconfigurable building parts.

Addressing catastrophic forgetting in payload parameter identification using incremental ensemble learning

Collaborative robots (cobots) are increasingly integrated into Industry 4.0 dynamic manufacturing environments that require frequent system reconfiguration due to changes in cobot paths and payloads. This necessitates fast methods for identifying payload inertial parameters to compensate the cobot controller and ensure precise and safe operation. Our prior work used Incremental Ensemble Model (IEM) to identify payload parameters, eliminating the need for an excitation path and thus removing the separate identification step. However, this approach suffers from catastrophic forgetting. This paper introduces a novel incremental ensemble learning method that addresses the problem of catastrophic forgetting by adding a new weak learner to the ensemble model for each new training bag. Moreover, it proposes a new classification model that assists the ensemble model in identifying which weak learner provides the most accurate estimation for new input data. The proposed method incrementally updates the identification model while the cobot navigates any task path, maintaining accuracy on old weak learner even after updating with new data. Validation performed on the Franka Emika cobot showcases the model’s superior accuracy and adaptability, effectively eliminating the problem of catastrophic forgetting.

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP).

Implementing AI Collaborative Robots in Manufacturing – Modeling Enterprise Challenges in Industry 5.0 with Fuzzy Logic

Implementing AI Collaborative Robots in Manufacturing – Modeling Enterprise Challenges in Industry 5.0 with Fuzzy Logic

Service Robots in the Workplace: Fostering Sustainable Collaboration by Alleviating Perceived Burdensomeness

This study investigates the impact of job demands and perceived burdensomeness on employees’ willingness to collaborate with service robots (SRs) in the workplace, with a focus on the moderating effects of organizational support, self-esteem, and self-development. Conducted among 200 employees in South Korea, the results show that higher job demands significantly increase perceived burdensomeness, which negatively affects employees’ willingness to collaborate with SRs. Organizational support moderates the negative impact of job demands, reducing their detrimental effects, although it was less effective in buffering the reduction in social interaction. Self-esteem and self-development positively moderated the relationship between burdensomeness and collaboration. This study contributes to the understanding of sustainable workforce management by highlighting the importance of fostering personal and organizational resources to ensure that SR integration enhances both operational efficiency and employee well-being. The findings align with broader sustainability goals by promoting human–robot collaboration that optimizes resource use, enhances workforce adaptability, and maintains social and psychological balance in the workplace.

Generative AI Copilot to Support Safety Analyses of Human–Robot Collaborations: Hazard Operability Analysis and GPT-4

Generative AI Copilot to Support Safety Analyses of Human–Robot Collaborations: Hazard Operability Analysis and GPT-4

The Synergistic Impact of Human-AI Collaboration: A Multi-Domain Analysis

This comprehensive article explores the transformative impact of human-AI collaboration across healthcare, manufacturing, and scientific research. The article examines how the synergistic integration of artificial intelligence with human expertise is revolutionizing key industries, enhancing efficiency, accuracy, and innovation. In healthcare, we discuss the implementation and impact of AI-assisted diagnostics and AI-augmented surgery, highlighting improvements in diagnostic accuracy and surgical outcomes. The manufacturing sector is analyzed through the lens of AI-driven quality control systems and the introduction of collaborative robots (cobots), demonstrating significant gains in productivity and worker safety. In scientific research, we explore AI's role in data-driven research and hypothesis testing, particularly in areas such as drug discovery and genomics. The article also provides a critical analysis of the complementary strengths of humans and AI, addresses ethical considerations in AI implementation, and speculates on the future potential of this collaboration. Drawing on recent research and case studies, this article presents a holistic view of how human-AI synergy is driving progress across multiple domains, while also considering the challenges and ethical implications of this technological integration. The findings suggest that the future of human-AI collaboration holds immense potential for accelerating scientific discovery, improving healthcare outcomes, and revolutionizing manufacturing processes, ultimately benefiting society as a whole.

Technology Gap Analysis on the BIM-Enabled Design Process of Prefabricated Buildings: An Autoethnographic Study

This research explores the pivotal role of Building Information Modeling (BIM) technology in revolutionizing the design and construction of prefabricated buildings. It highlights the benefits of BIM-enabled design processes, including improved coordination and efficiency, while addressing challenges in integrating BIM with prefabricated elements. The study proposes artificial intelligence (AI) solutions to enhance architectural detailing, optimize workflows, and facilitate human–robot collaboration. Using autoethnographic methods, the research refines BIM-forward design through thematic analysis and technology gap (TG) assessments. It advocates for a comprehensive technology roadmap that emphasizes BIM libraries, external data sources, and the transition to a multi-dimensional BIM environment. This research contributes valuable insights to advance BIM practices in prefabricated building construction and offers a strategic framework to bridge existing TGs, enabling seamless integration of BIM and AI in future practices.

Optimising acceptability using a user-centred design approach of a robotic software in Industry 4.0: A case study

While robotics and collaborative robotics are becoming increasingly commonplace in industry, operators do not always have full control over these tools. The aim of our robotics software is to give operators back the power to act. A user-centered design approach (UCD, ISO 9241-210, 2019), through four steps, has therefore been put in place within Industry 4.0. The first study aims to understand and specify the context of use through three objectives: to understand the organizational context in which the robotic software is to be designed and implemented, to characterize the end users via primary personas, and to take an interest in the operators' work in order to predict likely future situations and guide the development of the new work tool. The second study aims to specify user requirements using a card sort based on different criteria (ergonomic, UX). The third study presents the design solutions for version 2 of the robotics software. The fourth study evaluates and compares the practical acceptability (UX, usefulness, usability, mental load) and social acceptability (intent to use) of the first and second versions of the robotic software. Our results show, among other things, a significant improvement in acceptability for the second version of the software, suggesting greater acceptability of this technology thanks to the UCD approach.

Suitability of UR5 Robot for Robotic 3D Printing

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology.