https://www.mdpi.com/2218-6581/8/4/100/pdf

Human–Robot Collaboration in Manufacturing Applications: A Review

Reconfiguring and ramping-up ventilator production in the face of COVID-19: Can robots help?

The logistics market has been impacted by increase of e-commerce, mass customization, omni-channel distribution, and just-in-time philosophy. In order to attend to this dynamic change, automation has been applied in warehouses. Although, some researches point out the lack of flexibility as a bottleneck. Therefore, a comprehensive literature review of recent papers is vital to draw a framework of the past and to shed light on future directions. This paper aims to review published papers in the last ten years related to flexible automation in warehouses and to construct a framework that could guide future researchers in the construction of an innovative conceptual model that may be applied at warehouses in the future. A total of 113 papers published between January 2008 and December 2018 have been selected, reviewed, and categorized to construct a useful foundation of past research. Results showed the key point to achieve a flexible automated warehouse is the combination of automated equipment, data collection technologies, and management solutions. Finally, based on the reviewed papers, an innovative framework of a flexible automated warehouse is proposed.

Flexible automated warehouse: a literature review and an innovative framework

The teleoperation and coordination of multiple industrial robots play an important role in today’s industrial internet-based collaborative manufacturing systems. The user-friendly teleoperation approach allows operators from different manufacturing domains to reduce redundant learning costs and intuitively control the robot in advance. Nevertheless, only a few preliminary works have been introduced very recently, let alone its effective implementation in the manufacturing scenarios. To address the gap, this research proposes a novel multi-robot collaborative manufacturing system with human-in-the-loop control by leveraging the cutting-edge augmented reality (AR) and digital twin (DT) techniques. In the proposed system, the DTs of industrial robots are firstly mapped to physical robots and visualize them in the AR glasses. Meanwhile, a multi-robot communication mechanism is designed and implemented, to synchronize the state of robots in the twin. Moreover, a reinforcement learning algorithm is integrated into the robot motion planning to replace the conventional kinematics-based robot movement with corresponding target positions. Finally, three interactive AR-assisted DT modes, including real-time motion control, planned motion control, and robot monitoring mode are generated, which can be readily switched by human operators. Two experimental studies are conducted on (1) a single robot with a commonly used peg-in-hole experiment, and (2) the motion planning of multi-robot collaborative tasks, respectively. From the experimental results, it can be found that the proposed system can well handle the multi-robot teleoperation tasks with high efficiency and owns great potentials to be adopted in other complicated manufacturing scenarios in the near future. 

AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop

The assembly process of high precision products involves a variety of delicate operations that are time-consuming and energy-intensive. Neither the human operators nor the robots can complete the tasks independently and efficiently. The human-robot collaboration to be applied in complex assembly operation would help reduce human workload and improve efficiency. However, human behavior can be unpredictable in assembly activities so that it is difficult for the robots to understand intentions of the human operations. Thus, the collaboration of humans and robots is challenging in industrial applications. In this regard, a human-robot collaborative reinforcement learning algorithm is proposed to optimize the task sequence allocation scheme in assembly processes. Finally, the effectiveness of the method is verified through experimental analysis of the virtual assembly of an alternator. The result shows that the proposed method had great potential in dynamic division of human-robot collaborative tasks.

A reinforcement learning method for human-robot collaboration in assembly tasks

In the last decade, robot manufacturers have started to produce collaborative industrial robots, that can work while safely sharing the workspace with a human operator. In this way, robot repeatability, combined with human dexterity, can move automated assembly to a new level of flexibility. The aim of this paper is to investigate the conditions at which such systems, called collaborative assembly systems (CAS), can be better performing than the traditional manual or automated assembly systems. Throughput and unit direct production cost are considered for the comparison. The estimation of such performance figures, which is straightforward in traditional automated assembly systems, becomes more complex in the case of collaborative systems. In fact, both task allocation between the human and the robot, and the way they collaborate/interfere with each other during assembly, affect the throughput of CAS. With the aim of taking into account such parameters, we introduce a set of system variables and a mathematical model which allow to estimate the real convenience of the implementation of CAS in the industrial scenario. In the paper, the model is applied to compare CAS to manual assembly and to noncollaborative automated assembly, both with parameters derived from the literature and in a case study. Finally, a set of implementation conditions is derived, related to the task allocation that maximises CAS performance.

Collaborative and traditional robotic assembly: a comparison model

Collaborative assembly systems (CAS) are an increasingly important solution to the requests of the current market since they present the flexibility and dexterity of the human operator and the repeatability of the robot. For a CAS to be effective, both agents should carry out the tasks in the workspace without any physical interference. This paper aims to evaluate how the product characteristics influence the interference between the human operator and the robot. To reach the goal, we developed an algorithm that simulates product assembly in the considered workspace. The model also allowed to estimate the makespan achievable for different scenarios, showing a reduction, and thus an increase in the throughput. The makespan depends on several variables, related to the product characteristics and the process ones. Given specific conditions, we have observed that increasing the collaboration parameter from 21.06 to 48.98% led to a reduction in the makespan of about 20%. Lastly, the results obtained by the simulation were verified through a validation test, showing an error of − 2.39%.

The influence of the product characteristics on human-robot collaboration: a model for the performance of collaborative robotic assembly

Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling

The stiffness properties of industrial robots are very important for many industrial applications, such as automatic robotic assembly and material removal processes (e.g., machining and deburring). On the one hand, in robotic assembly, joint compliance can be useful for compensating dimensional errors in the parts to be assembled; on the other hand, in material removal processes, a high Cartesian stiffness of the end-effector is required. Moreover, low frequency chatter vibrations can be induced when low-stiffness robots are used, with an impairment in the quality of the machined surface. In this paper, a compliant joint dynamic model of an industrial robot has been developed, in which joint stiffness has been experimentally identified using a modal approach. First, a novel method to select the test configurations has been developed, so that in each configuration the mode of vibration that chiefly involves only one joint is excited. Then, experimental tests are carried out in the selected configurations in order to identify joint stiffness. Finally, the developed dynamic model of the robot is used to predict the variation of the natural frequencies in the workspace.

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

An elbow assisting device is presented as based on a cable-driven parallel mechanism with design solutions that are improvements from a previous original design. The new mechanism, ideal for domestic use, both for therapies and exercises, is characterized by low-cost, portable, easy-to-use features that are evaluated through numerical simulations and experimental tests whose results are reported with discussions.

Matteo Bottin

Papers

Collaborative and traditional robotic assembly: a comparison model

The influence of the product characteristics on human-robot collaboration: a model for the performance of collaborative robotic assembly

Trajectory Optimization of a Redundant Serial Robot Using Cartesian via Points and Kinematic Decoupling

Modeling and Identification of an Industrial Robot with a Selective Modal Approach

Design and Performance of an Elbow Assisting Mechanism