Saturday , June 23 2018

A Smart Robot Arm Design for Industrial Application

Faculty of Engineering, Mansoura University
35516, Egypt;

Abstract: The proposed paper outlines the design and implementation of smart robotic arm that is equipped with a vision system. Three main parts cooperate to perform the control of the proposed arm. Image processing, inverse kinematics and control are involved in the robot arm design. Forward and inverse kinematic are solved using homogenous transformation matrices and Denavit-Hartenberg’s systematic representation of reference systems. The arm uses vision information imported from the processing of the captured image of the target object to decide how to proceed. The proposed system is implemented on Microcontroller PIC 16F877a.The code involves securing the system from power failure as well as hacking. The controller has the ability to detect power failure and fix it allowing the arm to proceed from the stopping point avoiding starting over again (retentive). Practical application of the proposed system shows that its ability to handle different objects whether it is learned before or not is superior.

Keywords: Image processing, Inverse kinematics, Denavit-Hartenberg, Microcontroller, Robotic arm.

>>Full text
Amira Y. HAIKAL, Mostafa A. EL-HOSSEINI, A Smart Robot Arm Design for Industrial Application, Studies in Informatics and Control, ISSN 1220-1766, vol. 23 (1), pp. 107-116, 2014.

  1. Introduction

Factories and workshops which still lack the intelligence in their machines and still depend on human have bad effects either on human, due to health complications that may cause death, or on productivity. Previous problems lead to the use of intelligent robots with lower cost. The need for artificial intelligence and vision system is motivated by their capability to perceive the environment and take actions that maximize chances of success with the enterprise of automating and integrating a wide range of processes and representations for vision perception [1]. This article presents a self eyed robotic arm that can serve in many places with many applications like painting, welding, drawing and assembling. Robotic arm is used instead of human as it is more powerful, accurate, saves the materials, efficient in repetitive tasks, reduces the cost, and for all previous reasons this arm can improve productivity and quality of production.

Vision allows humans to perceive and understand the world surrounding them, while computer vision aims to duplicate the effect of human vision by electronically perceiving and understanding an image [1]. Giving computers the ability to see is not an easy task. Sometimes, equipment will deliver images that are 3D but this may be of questionable value: analyzing such datasets is clearly more complicated than 2D, and sometimes the ‘three-dimensionality’ is less than intuitive to us. Terahertz scans are an example of this [2]. Dynamic scenes such as those to which we are accustomed, with moving objects or a moving camera, are increasingly common and represent another way of making computer vision more complicated. Image processing involves changing the nature of an image in order to either improve its pictorial information for human interpretation or to render it more suitable for autonomous machine perception [3-7]. We are concerned with digital image processing, which involves using a computer to change the nature of a digital image. Humans like their images to be sharp, clear and detailed; however, machines prefer their images to be simple and uncluttered [8].

The inverse kinematic robotics problem has been the focus of kinematic analysis for robot manipulators. In order to determine all possible formations to place the end effector of a robot manipulator at a particular point in space, we must compute the movements associated with each joint variable [9]. Forward kinematics finds the value of the end position of the arm given the robot’s current joint angles. There are several methods to resolve this problem. In the presented work it is done using the homogenous transformation matrices and Denavit-Hartenberg’s systematic representation of reference systems. This is because although you may find the final position geometrically, this method offers a response which could relate the position of the end of each link in the kinematic chain compared to the previous reference system in order to define the position of each articulation in the robot [10]. Forward kinematics contributes to perform inverse kinematics and find joint angles that put the robot’s arm into a general desired position and orientation.

Actually, the forward kinematic problem uses the kinematics equations to determine the pose given the joint angles. While, the inverse kinematics problem computes the joint angles for a desired pose of the figure.

The smart arm implementation here is based on three main parts: Image processing, inverse kinematics and control. Where, image processing algorithm recognizes a new object then makes some processing and compares it with the stored ones in the database.

While inverse kinematics is responsible for obtaining the mathematical equations for converting the points representing the object’s coordinates, which come from the image processing algorithm, into angles for moving the motors of the arm. Finally, the control part is responsible for teaching the robotic arm manually the correct coordinates of the object (which are recorded) as well as, the set of movements to be retrieved later during a redo process. Moreover, the arm is controlled automatically after receiving the angles of the motors from inverse kinematics process which is implemented using PIC microcontrollers.

The rest of the paper is organized as follows: the proposed framework is presented in details in section 2. Section 3 covers the hardware implementation. Experimental results are illustrated in section 4.


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