Path planning for SCARA robot based on marker detection using feature extraction and, labelling

Vision guided robots have more ability, functionality and adaptivity in industrial assembly lines than normal robots. This research attempts to increase the impact of computer vision on robotic positioning and grasping applications. Therefore, we addressed object detection and localization to perform robotic grasping and positioning using Selective Compliant Assembly Robot Arm (SCARA). The target position of SCARA robot is determined based on information obtained from object detection and position measurement process. This process is implemented on a circular object to simplify the task. For accurate position measurement, the distortion of camera lens is removed using camera calibration technique. In object detection, several methods are compared to detect circular holes in an input image. The most successful methods with 100% Precision, Recall and F-measure are used to detect the circular object. The position of this object is measured in world coordinate unit for pick-and-place operation. Then, the experiment is designed to move SCARA robot to the measured position of the detected circular object. The result showed that the robot is successfully moved to the measured position of the detected object with average positioning error (0.314, 0.155) mm.

Starting oscillation or slow response will occur if the initial position of the motor used in the Selective Compliance Assembly Robot Arm manipulator is not known. To obtain the initial position of the motor, an improved high-frequency injection method is proposed. First, a high-frequency rotating voltage signal is injected into the stationary coordinate system, and the initial position information is extracted by the heterodyne method. Second, an improved position tracking observer is proposed to estimate the rotor position. Taking the joint of a four-axis Selective Compliance Assembly Robot Arm as an object, a special position servo drive system is designed. The design principle, simulation analysis and experimental verification of the proposed method are presented, respectively. The results show that the initial position of the motor can be estimated by the proposed method, enabling high torque and fast start of the Selective Compliance Assembly Robot Arm manipulator.

ABSTRACTThis study aims to enhance selective compliance assembly robot arm (SCARA) 3D printers to enable the reproduction of colors and the creation of multi‐colored 3D‐printed food. SCARA 3D printers offer a promising solution for multi‐colored food printing, overcoming the small printing spaces and poor scalability of delta 3D printers. A reversal valve was assembled onto the SCARA food 3D printer and synchronized with the movement of the robotic arm. This system realized collaborative two‐color ink printing, and its reversal positioning accuracy was tested. Additionally, composite printing was designed using SCARA's own programming language. The printed pancakes were subsequently processed in the kitchen to evaluate the effectiveness of the printing system. The reversal positioning accuracy test showed that the system has good precision for collaborative printing effectively. Kitchen processing of the printed pancakes results confirmed that the system can accomplish multi‐colored 3D food printing, and the quality loss of the printed product was nearly 45% under baking at 180°C for 20 min. The assembly of the reversal valve allows for collaborative printing, color reproduction, and the creation of multi‐colored 3D‐printed food. Furthermore, this study provides the equipment basis and preliminary research for the potential development of 3D‐printed cookies.

Design and development of dual-extruder food 3D printer based on selective compliance assembly robot arm and printing of various inks

The present paper deals with a precision inserting operation into the stepped hole without chamfer. Since there is a step in the hole, the peg-in-hole becomes a hard operation. To accomplish this complicated assembly, it is necessary to combine both active sensing and passive compliance. First, for active sensing, a force/torque sensor is used to recognize the contact state between parts. A SCARA (selective compliance assembly robot arm) corrects the lateral error from data of the sensor. Second, for passive compliance, the RCC (remote center compliance) device is used to avoid jamming and wedging. In this way, the algorithm of inserting a peg into the stepped hole without chamfer is constructed. This algorithm has three features : the use of the upper hole, the restriction on operational directions of the peg and the point-to-point operation between parts. The usefulness of this algorithm is shown by experimental results.

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

In this work the adaptive neuro-fuzzy inference system (ANFIS) controller is designed for the dynamic control (continuous path control) of the three-link selective compliant assembly robot arm (SCARA) manipulator. This ANFIS controller is designed to overcome the unmodeled dynamics and in the presence of structured and unstructured uncertainties of SCARA. The proposed controller-building technique combines artificial neural networks with fuzzy logic. The ANFIS control combines the advantages of neural networks (learning and adaptability) with the advantages of fuzzy logic (use of expert knowledge) to achieve the goal of robust control of robot dynamic systems. The fuzzy sets are used to formalize the level of human perception of the physical system. The neural network on the other hand performs all the necessary computations and regarding their learning capabilities, they enable an adaptation of the existing controller through its learning to the changes in the system behavior. The experimental ANFIS simulation results show very good SCARA tracking performance.

The SCARA (selective compliance assembly robot arm) robot has horizontal compliance. However, the amount and direction of compliance must be controlled before the robot can be used in actual applications. The authors have developed an active compliance controller that uses force sensors on the wrists of the robot to determine the contact forces acting on the end effector. The sensors feed this information to the trajectory generator of the robot controller. The controller achieves high-speed control (the sampling time is 1 ms) by performing all control operations in parallel using two MB8764 digital signal processors. The stability of the control was analyzed for joints having an arm that is not rigid. The force control can be stabilized by keeping the force feedback gain reasonably small. >

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

According to the robot’s dynamics, a high performance algorithm based on dynamic surface control is introduced to track desired trajectory, and simulations are conducted on a selective compliance assembly robot arm-type manipulator to verify the algorithm. The traditional dynamic surface control is designed based on dynamic model, which requires exact model information. Due to the model uncertainty and complex environments, the tracking performance of the controller can be significantly decreased. Therefore, a model-free fuzzy adaptive dynamic surface controller is designed, by adopting a fuzzy system with Lyapunov self-adaptation law. The new controller efficiently improves the dynamic quality. The simulation results prove that the designed model-free controller ensures that all the states and signals of the closed-loop system are bounded, the system has a faster response speed and smaller steady-state error comparing with the traditional dynamic surface control using the selective compliance assembly robot arm model, and the tracking error converge to a very small scale. Besides, the proposed algorithm can track the desired trajectory with high performance without the prior knowledge of specific parameters from the experimental manipulator, which simplifies the complexity of building the control system.

Aiming at the path planning for coal mine detection robot in unknown static environment, a path planning method is propounded which couples behavioural dynamics with rolling scan information. Firstly, the method of sub-target's choice is provided using heuristic function according to the rolling scan information L it is local environment information L which is obtained by sensors. Then path planning is divided into trending to target behaviour and obstacle avoidance behaviour L their behaviour state and behaviour pattern dynamics modes are established L and then one autonomous local path planning technology is implemented using behaviour dynamics mode in a single rolling scan region. Connecting a series of local paths tandem according to continuity, the global path planning is accomplished ultimately. The method has a simple principle and low calculating burden L smooth path and high applied value. The method's efficiency and adaptability is verified by simulation experiment. (5 pages)

Humans observe and infer things in a disentanglement way. Instead of remembering all pixel by pixel, learn things with factors like shape, scale, colour etc. Robot task learning is an open problem in the field of robotics. The task planning in the robot workspace with many constraints makes it even more challenging. In this work, a disentanglement learning of robot tasks with Convolutional Variational Autoencoder is learned, effectively capturing the underlying variations in the data. A robot dataset for disentanglement evaluation is generated with the Selective Compliance Assembly Robot Arm. The disentanglement score of the proposed model is increased to 0.206 with a robot path position accuracy of 0.055, while the state-of-the-art model (VAE) score was 0.015, and the corresponding path position accuracy is 0.053. The proposed algorithm is developed in Python and validated on the simulated robot model in Gazebo interfaced with Robot Operating System.

Vision-based control in robotics offers versatile automation; however, accessible educational platforms for exploring its integration with AI are still limited.This paper addresses this gap by presenting a small, 3D-printed parallel SCARA robot designed specifically for educational purposes.We provide details on its construction and demonstrate its application in laboratory exercises, which cover inverse and forward kinematics, vision-based tip positioning, and object detection.Notably, we investigate both supervised (using convolutional neural networks) and unsupervised (through autoencoder latent space exploration) approaches for classifying faulty parts.The unsupervised method achieved high performance, with a precision of 1.00, recall of 0.96, and an F1-measure of 0.98, which is comparable to the supervised approach that yielded a precision of 0.98, recall of 0.97, and an F1-measure of 0.97.This work contributes to the development of a low-cost platform and demonstrates the effectiveness of unsupervised AI techniques for vision-based robotic fault detection in educational settings, paving the way for more advanced AI-integrated robotics curricula.

This paper presents the concept of a novel telemanipulator for minimally invasive surgery, along with numerical analysis to validate the main system performance. The proposed kinematic structure consists of a passive and an active module. The passive module is similar to the Selective Compliance Assembly Robot Arm - SCARA robot. The active module is based on a parallelogram mechanism. The results of the numerical study are discussed, focusing on the influence of geometry parameters of the kinematic chain on the displacement accuracy of the end-effector. In particular, the paper deals with the identification of the main factors that impact the position accuracy of the robot.

This paper first establishes the kinematic model and dynamic model of Selective Compliance Assembly Robot Arm(SCARA) robot based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation, the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish the parameter identification. A genetic algorithm and particle swarm optimization(GAPSO) is introduced to overcome the problem of premature convergence. In GAPSO algorithm crossover operator and mutation operator of genetic algorithm are embedded in the process of PSO. Through GAPSO, the accuracy of parameter identification and convergence rate have been improved. Simulation studies show that this algorithm is more reliable and efficient than the least square method and basic PSO method.