Integrating Vision System to a Pick and Place Cartesian Robot

Vision aided pick and place cartesian robot is a combination of machine vision system and robotic system. They communicate with each other simultaneously to perform object sorting. In this project, machine vision algorithm for object sorting to solve the problem in failure sorting due to imperfection of images edges and different types of colours is proposed. The image is acquired by a camera and followed by image calibration. Pre-processing of image is performed through these methods, which are HSI colour space transformation, Gaussian filter for image filtering, Otsu’s method for image binarization, and Canny edge detection. LabVIEW edge-based geometric matching is selected for template matching. After the vision application analysed the image, electrical signal will send to robotic arm for object sorting if the acquired image is matched with template image. The proposed machine vision algorithm has yielded an accurate template matching score from 800 to 1000 under different disturbances and conditions. This machine vision algorithm provides more customizable parameters for each methods yet improves the accuracy of template matching.

Research on Multi-Object Sorting System Based on Deep Learning

As a complex task, robot sorting has become a research hotspot. In order to enable robots to perform simple, efficient, stable and accurate sorting operations for stacked multi-objects in unstructured scenes, a robot multi-object sorting system is built in this paper. Firstly, the training model of rotating target detection is constructed, and the placement state of five common objects in unstructured scenes is collected as the training set for training. The trained model is used to obtain the position, rotation angle and category of the target object. Then, the instance segmentation model is constructed, and the same data set is made, and the instance segmentation network model is trained. Then, the optimized Mask R-CNN instance segmentation network is used to segment the object surface pixels, and the upper surface point cloud is extracted to calculate the normal vector. Then, the angle obtained by the normal vector of the upper surface and the rotation target detection network is fused with the normal vector to obtain the attitude of the object. At the same time, the grasping order is calculated according to the average depth of the surface. Finally, after the obtained object posture, category and grasping sequence are fused, the performance of the rotating target detection network, the instance segmentation network and the robot sorting system are tested on the established experimental platform. Based on this system, this paper carried out an experiment on the success rate of object capture in a single network and an integrated network. The experimental results show that the multi-object sorting system based on deep learning proposed in this paper can sort stacked objects efficiently, accurately and stably in unstructured scenes.

Review Paper on Microcontroller Based Object Sorting

Sorting of products is a very difficult industrial process. Continuous manual sorting creates consistency issues. This paper describes a working prototype designed for automatic sorting of objects based on the metal detector KY-036 sensor was used to detect the colour of the product and the PIC16F628A microcontroller was used to control the overall process. The identification of the colour is based on the frequency analysis of the output of TCS230 sensor. One conveyor belts were used, it controlled by separate DC motors. The belt is for placing the product to be analysed by the colour sensor, having separated compartments, in order to separate the products. The experimental results promise that the prototype will fulfil the needs for higher production and precise quality in the field of automation.

Object Sorting Robotic Arm Based on Colour Sensing

An Object Sorting Robotic Arm Based on Colour Sensing is a technique used to sort and analyse different colours of objects, after that picking up and place on desired area or location. This application improved efficiency as well as reduce the manual workload. In the project, dc servo motors are used which rotate as per the program to complete the task. Total six dc servo motors for different parts of the robotic arm like waist, shoulder, elbow, wrist roll, Wrist, pitch gripper. All motors are connected to Arduino. Also used one colour sensor (TCS 3200)that is also connected to Arduino. The colour sensor used here is to sense red, green, blue colours.

Rancang Bangun Alat Pendeteksi Warna Menggunakan Arduino Uno Berbasis IoT (Internet Of Things)

The rapid development of technology causes a series of electronic applications to replace the role of humans as accuracy and accuracy in a job. Based on observations in the industrial sector, there are still few who use the services of human hands to sort an object. Sorting by color is one of them. This IoT-based color detector is a simulation of a tool designed to help ease human work in sorting an object. Generally, color detection techniques are still manual, requiring the user to adjust from where the tool is located. Therefore, we need a tool that can detect colors that can be adjusted through IoT-based applications (Internet of Things) using the TCS3200 color sensor as a color detector, Arduino Uno as Data Processing and Microsoft Visual Basic .NET applications as the media interface. Supported by the HMI (Human Machine Interface) system as a system that regulates the process of running a job, and a Servo Motor as a rotary actuator (motor) in directing a running object, color detection will be more accurate and efficient. So that users can get or make it easier to detect the color according to the desired amount.

Object Sorting using Faster R-CNN

In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.