Gesture Controlled Robotic Arm

Transcription

1 Gesture Controlled Robotic Arm Aishwarya Nagarkar Aishwarya Oza Rashmi Pandkar Abstract This paper presents the design of robotic arm which imitates the human hand movement to pick and place an object. The operators hand movement will be captured by camera and will be processed according to the colours (red, green, blue). On the basis of these color co-ordinates angle and slope is calculated. The data is sent serially through microcontroller at mega8 to the robotic arm. Depending upon the angle received the motor will move and hence the arm. There are two modes of operation. In one mode the robotic arm will simultaneously imitate the user s hand. In other mode it will perform the task on the basis of sequence of saved images. This system consists of atmega8 microcontroller, Camera, Servo motor, Motor Driver L293D. These patterns can potentially allow a robot to imitate from user s demonstration what is the task being executed and by whom and respond accordingly to reduce user effort. Thus current prototype tracks and mimics the human arm movements. As it imitates the human arm from images given the coding complexity is reduced. This prototype finds many applications in automobile industry, automation industry and some biomedical fields. INTRODUCTION Human life has become easy due to the evolution of Robots. Robots have helped in assisting humans in various jobs or even replaced them with regards to both physical activity and decision making. This century has witnessed the growth of robotics like never before. The fact that most physical and computational work is being done by machines and has changed the way the world runs. Industries have witnessed the automation of its processes, increasing the efficiency and decreasing time and human labor. Electronics has also progressed with the coming of this age, as new technologies were discovered for cheap fabrication & circuit realization. Under extreme conditions, it is likely that humans will fail to respond in an effective way. This is due to the physical & mental limitations humans have. In such situations we can use robots to execute the same task in a more productive way. This can also lead to life-saving operations. Real world examples include rescue operations for the military and robotic surgery in the medical field. This paper presents a different methodology to move the robotic arm by capturing the human hand. OBJECTIVE The main objective of our project is to interface camera and robotic arm with the help of microcontroller and this entire process will be carried through serial communication. The robotic arm can be used to perform any type of operation such as welding, gripping, etc. from a functional point of view there are two main requirements that an artificial hand is supposed to accomplish- dexterous manipulation and grasping. 292 AishwaryaNagarkar, AishwaryaOza, RashmiPandkar

2 METHODOLOGY For the real time analysis, a camera will capture the image of the hand. This will do the real time image processing by the MATLAB which will track the colors stick on the hand by image subtraction method. Figure 1. Block Diagram Working For tracking of the hand we are using 3 types of colors that are RGB (Red, Green and Blue). Now this part comes under image processing. Here the angles are calculated depending upon the X and Y axis of the particular color. Now the image processing part is over. Then these signals are given to the Arduino for interfacing. Arduino will interface the MATLAB with robotic arm for real time movement of arm by the movement of the human hand. This procedure will be going to take place with interfacing with Arduino and MATLAB through serial communication. This Arduino will track the interfacing angles to control the robotic arm. A. Tracking of colors: Colors are tracked using the three basic colors- RGB. They are chosen because every image in this world is made up of these three colors. The colors are assigned in such a way that shoulder to elbow will get one color, elbow to wrist second and ahead of wrist, the third color. To convert the image from normal to grey image, first we convert the 3D image into 2D image. Color tracking can be done by the method of image subtraction. In this method the colors such as RGB can be subtracted from the background colors and the remaining color is RGB color. After proper thresholding technique, we can get the required image. B.Formation of axis: These basic colors are then assigned with their respective X and Y axis according to their pixel positioning. This axis will be takes according to the position of the centroid point of the shape of the color. Whatever is the shape of the color, even if it is a square, rectangle, and circle or of any shape, its centroid will decide the position of the axis. The pixel value of the centroid position is going to decide the X and Y axis of the color shape. C.Angle calculation: The position of the angle from the axis is calculated by trigonometric equations. These angles will tell the degree of rotation of our hand and its direction. The three axial position gives the rotation of arm in any direction and perform variousfunctions. To move the robotic arm, the co-ordinates and slope are sent from MATLAB to the micro controller. The speed to rotate the motors is obtained from the slope and hence the robotic arm moves. Angles are calculated to move our robotic arm depending upon the movement of our hand. Angles can be calculated depending upon the position of axis. This axis calculated by the position of centroid from the shape. 293 AishwaryaNagarkar, AishwaryaOza, RashmiPandkar

3 The angles can be calculated by the simple trigonometric equations. One of the equations for calculation of angles can be considered by finding the slope between the lines, which is given as: tanq = m1-m2/ 1 + m1*m2 where, m1= slope of first line m2= slope of second line tanq= angle between the joint Figure 2. Flowchart SPECIFICATIONS HARDWARE USED: 1)Atmega 328p Output voltage = 5V Input voltage = 7V 12V (limits = 6V-20V) DC Current per I/O pin = 40mA, 50mA for 3.3V pin 16MHz clock speed 2KB SRAM, 1KB EEPROM,32KB FLASH memory 6 Analog pins, 14 (6 PWM) pins 2) Motor Driver (L293D) 16 pin Supply voltage = 5V 12V (up to 36V) 600mA to 1ACurrent 294 AishwaryaNagarkar, AishwaryaOza, RashmiPandkar

4 3) DC Motor (12V, 600mA), Servo Motor (5V, 600mA) 4)Robotic Arm SOFTWARE USED: MATLAB Version 13 Arduino IDE Proteus RESULTS Figure 3. Calculation of Co-ordinates & Slope FUTURE PLANS The current prototype focuses on the movement of the arm only. A system can be implemented in which entire body movements can be imitated. Future techniques may also The current prototype focuses on the movement of the arm only. A system can be implemented in which entire body movements can be imitated. Future techniques may also reduce the delay factor or the lag factor. It can find many applications in defense, biomedical numerous fields. CONCLUSION In this paper the robotic arm imitates the human hand. This is a very simple method to increase man-machine interface. To control the robotic arm, optimum lightning conditions are required. So that there will be less error while recording the video. This paper presented the design of robotic arm which imitates the human hand movement to pick and place an object. The robotic arm is self-activated which means that it will drive itself according to position of human arm. It tracks & mimics the human arm gesture. Hardware required is easily available & software is an open source. This prototype finds many applications in automobile industry, automation industry and some biomedical fields. 295 AishwaryaNagarkar, AishwaryaOza, RashmiPandkar

5 REFERENCES 1. Design of a gripping imitator robotic arm for taking an object (Ali Rizal Chaidir; Alfredo BayuSatriya; Gyido Dias Kalandro) th International Conference on Information & Communication Technology (ICoICT), 22nd September Complex Task by Users with upper-extremity disability using a 6-DOF robotic arm: A Study. (Reem Al Halimi; Mehdat Moussa) IEEE Transactions Neural Systems & Rehabilitation Engineering, 26th August Survey of Robotic Arm & Parameters (ViendarPatidar; Ritu Tiwari) 2016 International Conference on Computer Communication & Informatics (ICCCI), 30th May Review on design & development of intelligent robotic arm (Netra Barai; Swati Manekar) Intelligent Systems & Control (ISCO), 2015 IEEE 9th International Conference, 1st October Kinect based Gesture controlled Robotic Arm: A Research Work at HuT Labs (Rajesh Kannan Megalingum, Nihil Saboo, Nitin Ajith Kumar, SreeramUnny, Deepansh Menon) 18th February AishwaryaNagarkar, AishwaryaOza, RashmiPandkar