Multi-Criteria Design Optimization of Delta Robot with Four Degrees of Freedom

A delta robot with three degrees of freedom, having been well studied over the past 40 years, is one of the most popular parallel mechanisms. Nowadays, an urgent task is to study the properties of various modifications of this mechanism. The article considers a delta robot with four degrees of freedom, in which one of the kinematic chains with a parallelogram is divided into two, allowing the output link to have an additional rotational degree of freedom. To maximize the working area and minimize the cost of modification the optimization of the robot design was performed. The problem of maximizing a cubic workspace has been solved.

Design and Implementation of a Low-Cost Microcontroller-Based an Industrial Delta Robot

This paper presents a practical design and control for a delta robot based on a low-cost microcontroller. The main purpose of the proposed delta robot is to improve and enhance industrial productivity such as fast pick-and-place tasks and fully autonomous production lines. Additionally, during a global pandemic similar to (COVID-19), some medical and food products suffer from a sudden increase and demand. Moreover, kinematics, workspace dynamics analysis took into consideration an optimized approach to achieve a viable yet efficient model representing them. Furthermore, stress analysis and material selection have been applied, targeting to achieve high customizability of the manipulator linages. Taking availability into considerations, most components are available locally for ease of manufacturing. To add a touch of machine vision to the robot, a camera module is mounted in an optimized fashion to optimize the robot's performance and increase its accuracy. Finally, various interchangeable end effectors can be mounted including a magnetic gripper, vacuum suction cup, soft-robotics grippers, and other types to suit our requirements and needs.

Modeling the Delta Robot Movement along a Specified Trajectory in order to Determine the Force Factors Acting on its Drives and Hinges

Improving the productivity of machinery and auxiliary equipment has long been one of the main directions for the world industry development. Efforts to gain fractions of a percent of the indicator require both the improvement of existing mechanisms and the introduction of faster manipulators, such as a delta robot. One of the key design tasks for such mechanisms is determining the required drive characteristics. The article presents a solution of the inverse kinematics problem for a delta robot. An algorithm for planning the movement of the working body for performing a typical operation of object permutations is described. The issues of modeling the movement of a robot in the computer-aided design system Autodesk Inventor are considered. The dynamic characteristics of the manipulator have been obtained, on the basis of which it is possible to select drives, bearings and kinematic pairs.

Improving the accuracy of designing a delta robot for 3D printing

Currently, there is a need in the industry for design changes to existing installations, such as conveyor lines, various machine tools, 3D printers, and so on. Designing delta robots for 3D printers reveals the advantages of using delta robots as working parts of printers compared to traditional designs. In this article, the direct and inverse problems of the kinematics of the delta robot are solved in a geometric way. Also, dependencies for the search for angular velocities and accelerations of the input links were obtained, which allows in the future to design more accurate working bodies of 3D printers. The research was carried out through mathematical modeling.

Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.