A Review of Intelligent Assembly Technology of Small Electronic Equipment

Electronic equipment, including phased array radars, satellites, high-performance computers, etc., has been widely used in military and civilian fields. Its importance and significance are self-evident. Electronic equipment has many small components, various functions, and complex structures, making assembly an essential step in the manufacturing process of electronic equipment. In recent years, the traditional assembly methods have had difficulty meeting the increasingly complex assembly needs of military and civilian electronic equipment. With the rapid development of Industry 4.0, emerging intelligent assembly technology is replacing the original "semi-automatic" assembly technology. Aiming at the assembly requirements of small electronic equipment, we first evaluate the existing problems and technical difficulties. Then, we analyze the intelligent assembly technology of electronic equipment from three aspects: visual positioning, path and trajectory planning, and force-position coordination control technology. Further, we describe and summarize the research status and the application of the technology and discuss possible future research directions in the intelligent assembly technology of small electronic equipment.

HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics

The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers' support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers' well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker's models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers' health information towards a successful risk management strategy for safe industrial Cobot environments.

A compliance modeling method of flexible rotary joint for collaborative robot using passive network synthesis theory

Collaborative robots have become a research focus because of their wide applications. However, the previous compliance design method of the flexible rotary joint for collaborative robot mainly relied on experience of designers, and “trial and error” method is usually adopted, no feasible and systematic theory for the designer to select numerical value and series-parallel connection mode of the springs and dampers for the flexible rotary joint. Thus, developing a feasible compliance modeling theory to guide the design of the flexible rotary joint is a particularly challenging task. The main contribution of this paper is to present a novel and effective compliance modeling theory of the flexible rotary joint for collaborative robot based on electrical and mechanical passive network synthesis, to provide theoretical and systematic guidances for compliance design of the flexible rotary joint. First, inerter element is introduced into the mechanical system, and the compliance of the flexible rotary joint is expressed as an angular velocity admittance function using electrical and mechanical network analogy. Then, by passive network synthesis theory, the three kinds of compliance realization forms of rational function and four-element compliance realization conditions of biquadratic function for the flexible rotary joint are given using inerters, springs, and dampers. Moreover, numerical examples and simulations are conducted to illustrate effectiveness of the proposed compliance realization method. Finally, discussions are given to illustrate advantages of the proposed compliance modeling and design methods compared with the previous method.

Digital Twin-Driven Human Robot Collaboration Using a Digital Human

Advances are being made in applying digital twin (DT) and human–robot collaboration (HRC) to industrial fields for safe, effective, and flexible manufacturing. Using a DT for human modeling and simulation enables ergonomic assessment during working. In this study, a DT-driven HRC system was developed that measures the motions of a worker and simulates the working progress and physical load based on digital human (DH) technology. The proposed system contains virtual robot, DH, and production management modules that are integrated seamlessly via wireless communication. The virtual robot module contains the robot operating system and enables real-time control of the robot based on simulations in a virtual environment. The DH module measures and simulates the worker’s motion, behavior, and physical load. The production management module performs dynamic scheduling based on the predicted working progress under ergonomic constraints. The proposed system was applied to a parts-picking scenario, and its effectiveness was evaluated in terms of work monitoring, progress prediction, dynamic scheduling, and ergonomic assessment. This study demonstrates a proof-of-concept for introducing DH technology into DT-driven HRC for human-centered production systems.

A Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction

Human–Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist human workers in their tasks and improve their efficiency. However, the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC)-based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

Toward safe and high-performance human–robot collaboration via implementation of redundancy and understanding the effects of admittance term parameters

Today, demandsin industrial manufacturing mandate humans to work with large-scale industrial robots, and this collaboration may result in dangerous conditions for humans. To deal with this situation, this work proposes a novel approach for redundant large-scale industrial robots. In the proposed approach, an admittance controller is designed to regulate the interaction between the end effector of the robot and the human. Additionally, an obstacle avoidance algorithm is implemented in the null space of the robot to prevent any possible unexpected collision between the human and the links of the robot. After safety performance of this approach is verified via simulations and experimental studies, the effect of the parameters of the admittance controller on the performance of collaboration in terms of both accuracy and total human effort is investigated. This investigation is carried out via 8 experiments by the participation of 10 test subjects in which the effect of different admittance controller parameters such as mass and damper are compared. As a result of this investigation, tuning insights for such parameters are revealed.

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

Fenceless obstacle avoidance method for efficient and safe human–robot collaboration in a shared work space

In a given manufacturing setting where workers or robots are coexisting in a confined area and their movements are not coordinated due to loss in communication or because they are freely ranging relative to each other, the development of an onboard safeguard system for a robot becomes a necessity to reduce accidents while the production efficiency is uncompromised. This article develops a two-dimensional dynamics model that predicts the relative position between a robot’s end-of-arm tooling and an approaching object or threat. The safety strategy applied to the robot is derived from the calculation of three parameters: the time of collision predicted from the linear motion between the approaching object and the robot’s end-of-arm tooling, the relative absolute distance, and the overlapping area ratio. These parameters combined are updated in a cost function that is sufficiently alarming the collision severity of an approaching object in real time. This model enables deployment a safe and a productive collaborative interaction in the manufacturing environment where workers and robots are seemingly moving in close proximity within an open workspace with less safeguard barriers.

A Multi-Objective Trajectory Planning Method for Collaborative Robot

Aiming at the characteristics of high efficiency and smoothness in the motion process of collaborative robot, a multi-objective trajectory planning method is proposed. Firstly, the kinematics model of the collaborative robot is established, and the trajectory in the workspace is converted into joint space trajectory using inverse kinematics method. Secondly, seven-order B-spline functions are used to construct joint trajectory sequences to ensure the continuous position, velocity, acceleration and jerk of each joint. Then, the trajectory competitive multi-objective particle swarm optimization (TCMOPSO) algorithm is proposed to search the Pareto optimal solutions set of the robot’s time-energy-jerk optimal trajectory. Further, the normalized weight function is proposed to select the appropriate solution. Finally, the algorithm simulation experiment is completed in MATLAB, and the robot control experiment is completed using the Robot Operating System (ROS). The experimental results show that the method can achieve effective multi-objective optimization, the appropriate optimal trajectory can be obtained according to the actual requirements, and the collaborative robot is actually operating well.