Transfer Robot Research Articles

Path Planning for Shop-Floor Material Transfer Robots Incorporating Particle Swarm and Ant Colony Algorithms

Path Planning for Shop-Floor Material Transfer Robots Incorporating Particle Swarm and Ant Colony Algorithms

An Efficient Motion Adjustment Method for a Dual-Arm Transfer Robot Based on a Two-Level Neural Network and a Greedy Algorithm

As the manipulation object of a patient transfer robot is a human, which can be considered a complex and time-varying system, motion adjustment of a patient transfer robot is inevitable and essential for ensuring patient safety and comfort. This paper proposes a motion adjustment method based on a two-level deep neural network (DNN) and a greedy algorithm. First, a dataset including information about human posture and contact forces is collected by experiment. Then, the DNN, which is used to estimate contact force, is established and trained with the collected datasets. Furthermore, the adjustment is conducted by comparing the estimated contact force of the next state and the real contact force of the current state by a greedy algorithm. To assess the validity, first, we employed the DNN to estimate contact force and obtained the accuracy and speed of 84% and 30 ms, respectively (implemented with an affordable processing unit). Then, we applied the greedy algorithm to a dual-arm transfer robot and found that the motion adjustment could reduce the contact force and improve human comfort efficiently; these validated the effectiveness of our proposal and provided a new approach to adjust the posture of the care receiver for improving their comfort through reducing the contact force between human and robot.

Human–robot mechanics model using hip torque identification with a dual-arm nursing-care transfer robot

In response to the growing demand for robotic interventions to mitigate the profound physical strain experienced by caregivers during transfers, dual-arm robotic systems have emerged as a focal point of interest in the caregiving community due to their adaptability and versatility. However, the accuracy of existing human–robot mechanics model is insufficient, impacting the execution of transfer tasks. To enhance the model’s precision, an improved model is proposed, integrating hip torque identification. This proposed methodology involves the simplification of the human structure based on the kinematic attributes associated with the embrace of individuals by robotic arms and the subsequent computation of its inertial parameters. Accordingly, the application of D’Alembert’s principle is employed to analyze the impact of embracing motion, human posture, and the position of human–robot contact on the forces exerted on the human. This culminates in the establishment of a model. Considering the static indeterminacy predicament inherent in the model, a biomechanical data set is curated for the dual-arm transfer scenario. Leveraging this data set, a deep neural network utilizing multilayer perceptron is trained to accurately identify hip torque, thereby improving the model. Accordingly, a robotic transfer platform featuring dual arms is developed and trailed on six subjects with varying anatomical profiles. The results show that the constructed model has high accuracy. This study provides critical mechanics insights for dual-arm transfer tasks, offering potential application value in the field of nursing and rehabilitation.

CSAPSO-BPNN-Based Modeling of End Airbag Stiffness of Nursing Transfer Robot

The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human–robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then, the performance of the Back Propagation (BP) neural network is improved using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load–displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its three distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags.

Predictive Maintenance System for Wafer Transfer Robot Using Gaussian Mixture Model and Mean-Shift Clustering

Equipment maintenance is the technology of monitoring the condition of equipment and predicting the error of the equipment. As an interest in equipment maintenance increases, many studies are being conducted on methods for minimizing economic losses due to equipment failure or enormous maintenance costs, especially in the semiconductor manufacturing field. In this paper, a new predictive maintenance (PdM) method is proposed to predict the error of the wafer transfer robot and to classify the significance level of the error. The experimental data of the wafer transfer robot were collected using acceleration sensor. To analyse the data, pre-processing was performed using fast Fourier transformation (FFT). The error levels were defined by classifying the number of error areas into 1, 2 and 3 regions. The Gaussian mixture model (GMM) algorithm was used to classify normal and abnormal states and mean-shift (MS) algorithm was used to classify the error level for the data classified as abnormal state. The proposed method can detect errors and classify the error level accurately in the data where normal data and error data were mixed.The advantage of the proposed method is that it can accurately distinguish errors from normal data by applying GMM with higher accuracy that previous studies and classify the level of errors using MS.

Human-Comfort Evaluation for a Patient-Transfer Robot Through a Human–Robot Mechanical Model

Abstract Patient-transfer robot, which can transfer a bedridden care receiver from a bed to a wheelchair or a pedestal pan and back, was not widely used due to inadequate safety and comfort. A human comfort evaluation function based on force analysis was proposed to improve the comfort of a dual-arm transfer robot. First, a human–robot mechanical model was construct by simplifying the structure of human body and the robot. Then, the internal and external forces acting on human body were calculated by the developed human–robot mechanical model. After that, a comfort evaluation function was established through mechanical analysis and a questionnaire investigation method. To assess the validity of the proposed method, first we employed the comfort evaluation function to estimate human comfort, and obtained that the comfort level is proportional to the electromyography (EMG) signal and pressure signal of human. Then we applied the function to a dual-arm patient-transfer robot to optimizing lifting points and transfer posture and found it can effectively reduce the human–robot contact force and the burden of the waist by 44.2%, improving the comfortability of the care receiver.

System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes

In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from the motor control signal acquired during the operation, diagnosing the current health of each component using the features, and estimating the associated degradation in the robot system’s performance. Finally, a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. The framework is demonstrated using the example of a wafer transfer robot in the semiconductor industry, in which the robot is operated under faults with various severities for two critical components: the harmonic drive and the timing belt. Features are extracted for the motor signal using wavelet packet decomposition, followed by feature selection by considering the trendability and separability of the fault severity. An artificial neural network model and Gaussian process regression are employed for the diagnosis of the components’ health and the system’s performance, respectively.

Design of integrated robot for risk waste transfer and disinfection

In order to solve the traditional elimination process cumbersome, manual cannot involve part of the hidden places, single function, and other problems, we designed an integrated risk garbage transfer and elimination robot. The robot is integrated based on the parent and child machines. The mother robot completes the garbage transfer task and the child robot completes the indoor elimination task. With DJI STM32-F4 series as the core board, the integrated robot control system is composed of ultrasonic obstacle avoidance technology. This system can automatically identify and transport garbage, spray disinfectant and autonomous navigation for unmanned elimination. The combination of the integrated mother robot greatly improves the efficiency of elimination and provides support for the direction of disinfection and cleaning robots.

An Energy Consumption Optimization Method for Intelligent Transfer Robots in a Low-temperature Environment

The functional design and performance parameters of existing intelligent transfer robots (ITRs) have met the operational requirements for most scenarios at present. However, under the operation condition of low temperatures, the battery discharge capacity of ITRs will significantly reduce, resulting in a decrease in the efficiency of ITRs. Aiming at this problem, this paper proposes an energy consumption optimization method for intelligent transfer robots in a low-temperature environment, which can make the battery discharge capacity meet the best performance by adjusting the motion parameters of ITRs. Firstly, the optimal output current is obtained by analyzing the output characteristics of the battery in a low-temperature environment, and it is measured that the energy consumption of ITRs mainly depends on the energy used in the moving process. Then, it is proved that the moving speed of the ITR has the greatest influence on the output current of the battery based on comparative experiments. Next, the LSTM prediction model and fuzzy PID control are used to regulate the moving speed of the ITR, so that the output current of the robot battery can meet the optimal output performance in a low-temperature environment. Finally, extensive comparative experiments show that the energy consumption optimization method proposed in this paper can effectively improve the discharge capacity of robot batteries in a low-temperature environment.

An Asymmetric Velocity Profile for Minimizing Wafer Slippage and Settling Time of a Wafer Transport Robot

This paper presents a control solution for minimizing the takt time of a wafer transfer robot that is widely used in the semiconductor industry. To achieve this goal, this work aims to minimize the transfer time while maximizing the transfer accuracy. The velocity profile is newly designed, taking into consideration parameters such as end effector deformation, changes in friction, vibrations, and required position accuracy. This work focused on the difference between the robot’s acceleration and deceleration phases and their contributions to wafer dynamics, resulting in an asymmetric robot motion profile. Mixed cubic and quintic Bezier curves were adopted, and the optimal profile was obtained through genetic algorithms. Additionally, this work combines its newly developed motion profile with an iterative learning control to ensure the best wafer transportation process time. With the presented method, it is possible to achieve a significant reduction in takt time by minimizing wafer slippage and vibration while maximizing robot motion efficiency. All development processes presented in this paper are verified through both simulation and testing.

Goods Movement Robot Prototype Design With Wheel Arm System

One of the uses of robotics technology that is developing is in the field of warehousing, with robots transferring vehicle goods used in the industrial world, especially in the warehousing section. The item transfer robot has 2 navigations, namely on the wheeled part, namely the robot can maneuver in all directions without the need to rotate the body of the robot (holonomic robot). Then one of the robots that is used quite often in the industrial world is the arm manipulator. Robots that have a physical shape like a human arm and a degree of freedom (Degree of Freedom). The transfer robot system is controlled remotely using an IoT-based smartphone using the ESP32 Wemos D1 R32 module as the robot's driving brain and ESP32-Cam as the robot's drilling visual. Several robot tests are carried out to ensure that the designed robot can run properly. From the results of functional testing, parts of the robot can run well. The robot can walk through commands from a smartphone, the gripper on the arm manipulator can grasp objects and the ESP32-Cam can display images to the smartphone.

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment.

Slope Running Experiment of a Transfer robot with Sub-crawlers

Slope Running Experiment of a Transfer robot with Sub-crawlers

Influence of Dynamic Accuracy Constraints of Manipulator of Wafer Transmission Robot on Scheduling and Control of Single-Armed Cluster Tools

The wafer transfer robot is a key part of integrated circuit equipment which performs the transit of wafers precisely, quickly and steadily. The dynamic accuracy of the manipulator of the wafer transfer robot directly affects the quality of transferring and processing wafers and even the scheduling and control of the cluster tools. Thus, it is essential to study the influence of the dynamic accuracy of the manipulator of wafer transmission robots on the scheduling and control of cluster tools. In this paper, single-arm cluster tools are taken as the research object. The horizontal torsional vibration equations of the manipulator of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> robot are constructed, and the torsional vibration attenuation characteristics of the manipulator are analyzed. Based on the torsional vibration equations, the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator are explored when the manipulator loads and unloads the wafers. Then the two-stage approach is proposed for the scheduling and control of single-arm cluster tools. The first stage determines the minimum waiting times of the manipulator according to the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator when the manipulator is waiting for loading and unloading wafers in each processing module and load lock. The second stage achieves the scheduling optimization and control of single-arm cluster tools with dynamic accuracy constraints and wafer residency time constraints by establishing a mathematical programming model for the scheduling and control of single-arm cluster tools. Finally, illustrative examples are presented to analyze the influence of the dynamic accuracy of the manipulator on the scheduling and control of single-arm cluster tools.

Bio-inspired dry adhesive pads using multi-walled carbon nanotube/polydimethylsiloxane composites for efficient wafer transfer robot arms in smart factories

We report the bio-inspired dry adhesive pads (BDAPs) with various shapes and contact areas optimized for various robot arm movements. Moreover, MWCNT/PDMS composite-based BDAP (c-BDAP) has shown superior heat resistance and electrical conductivity.

Wafer Center Alignment System of Transfer Robot Based on Reduced Number of Sensors.

This brief presents a wafer alignment algorithm with reduced sensor number that obtains the relative distance of the wafer center and the robot hand. By 'reduced number', in spite of smaller number of sensors than the conventional method, we mean an improved method which achieves the similar results to pre-existing algorithms. Indeed, it can be designed with only three sensor data, less than four sensors of the conventional algorithm. Thus, some advantages of the proposed alignment algorithm include that it can be designed with low cost and less computing power. The proposed alignment algorithm is applied to a transfer robot for coater/developer system in semiconductor processing for verifying the performance of the method. The performance of the proposed method is validated by both simulation and experimental results.

A New Configuration Method for Glass Substrate Transfer Robot Modules Based on Kansei Engineering

How to effectively match the relationship between users’ perceptual demands and the characteristics of industrial robot modules becomes a pressing issue when perceptual demands become a significant determinant of whether users purchase and employ industrial robots. In this regard, we propose a Kansei Engineering-based method for industrial robot module configuration, using the module design of a glass substrate transfer robot as an example. First, the method analyzes the perceptual demand characteristics of the target user, utilizing the semantic difference method, and then establishes a mapping relationship between the user’s perceptual demand and the robot design elements, utilizing the hierarchical inference method. On the basis of this mapping relationship, the robot module for transfer glass substrates is then designed. Finally, orthogonal design and conjoint analysis were used to effectively and objectively analyze user preferences for various module configuration alternatives. The results indicate that the industrial robot’s shape, color, and material are the three appearance characteristics that influence the user’s perceptual demands. The slender, rigid design features of the industrial robot, such as the streamlined drive shaft, lengthwise expanded body structure, integrated body structure, and hidden plugs, as well as the simple color scheme and smooth metal surface, are key elements in the industrial robot’s perceptual design. The turn shaft module and lift shaft module have respective weights of 35.040% and 31.120%, determining whether the glass substrate transfer robot can create a simple style. In the context of the widespread use of industrial robot modules, the methods and findings of this study offer new ideas for the design of industrial robot modules and broaden the research and applications of Kansei Engineering in module design.

A Bioinspired Adhesive Sucker with Both Suction and Adhesion Mechanisms for Three-Dimensional Surfaces

There are significant advantages to investigate underwater attachments, which would be valuable in providing inspirations and design strategies for multi-functional surfaces and underwater robots. Here, an abalone-inspired sucker integrating an elastic body and a membrane structure is proposed and fabricated filled with rigid quartz particles to adjust the backing stiffness of the contact like abalone. The membrane is used to conform and contact surfaces well, the center area of which can be pulled in exposed to a negative pressure differential, to create a suction cavity. The pulling experiments indicate that the sucker can adhere to three-dimensional surfaces with both suction and adhesion mechanisms in both dry and liquid environments. The switching between soft/hard contact states leads to the change of adhesive strength over 30 times. Furthermore, we provide theoretical analysis on how the sucker work well in both dry and liquid environments. Finally, the developed sucker can easily lift up smooth planar objects and 3D objects, and can grip objects both smaller and larger than the size of the sucker, which have a difficulty for conventional suckers or friction-based grippers. The potential application of the sucker in flexible transfer robot is demonstrated on various surfaces and environments, paving the way for further bio-inspired adhesive designs for both dry and wet scenarios.

Mechanics Model-Based Motion Design for a Piggyback Nursing-Care Robot

To conduct a comfortable lift for the care-receiver, it takes a lot of time and operations to design the motion trajectory for each care-receiver before transfer tasks. To solve this problem, this paper proposed a method to design a lift trajectory for a piggyback transfer robot. The robot, which can lift and move a person from a wheelchair to a bed or a pedestal pan, has been developed. The trajectory obtained by this method could make the robot conduct a comfort lift for the care-receiver, according to the weight and height of the care-receiver. A human-robot mechanics model and the relationship between the comfortable lift trajectory and the care-receiver’s weight and height were also contributed. According to the test results of 20 subjects, the force parameters used for trajectory design were determined, and the trajectory design method was optimized. The results of three subjects demonstrated that this method could conveniently and quickly provide a robot lift trajectory based on the subject’s weight and height, and this trajectory also achieved a similar lift as the trajectory designed by relying on the opinion of the subject. This method can be used for the design of the reference trajectory in the compliant control of the piggyback robot, which realizes the comfortable lifting of the care-receiver.

User Intention Based Intuitive Mobile Platform Control: Application to a Patient Transfer Robot

User Intention Based Intuitive Mobile Platform Control: Application to a Patient Transfer Robot