Evolutionary Robot Vision for People Tracking Based on Local Clustering

This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and a human tracking method based on the evolutionary robot vision. Finally, we show experimental results of the human tracking to discuss the effectiveness of our proposed method

Motion Optimization of Humanoid Robot Soccer “Goalkeeper” Using Behavior Based Coordination

Accuracy and speed of movement is required for a goalkeeper robot in the Robocup soccer competition. Moreover, instability and robustness of goalkeeper robot is often a problem in itself that often arise especially if the robot is a humanoid robot. There are various methods on how to improve the performance of movement of humanoid robot have been actively studied. So now we propose how to optimize the movement of humanoid robot and research to this time is devoted to the movement of a humanoid robot goalkeeper by using behavior based coordination. In this paper, a stabilization algorithm is proposed using the balance condition of the robot, which is measured using accelerometer sensor during standing, walking, turning, getting up, etc. Then the information from the outside is obtained by using the other sensor that is webcam camera and also from this sensor the robot can decide and behave to respond the data information effectively. In order to generate the proper and fast reaction, so a behavior based algorithm is applied in finding the most effective movement when the robot responds some stimulus. The performance of the proposed algorithm is verified by walking, getting up and ball anticipating movement and this experiment is conducted on a 16-DOFs humanoid robot, called EEPIS Fußball Robot IO (EFuRIO) 2nd generation

Visual Perception for a Partner Robot Based on Computational Intelligent

We propose computational intelligence for partner robot perception in which the robot requires the capability of visual perception to interact with human beings. Basically, robots should conduct moving object extraction, clustering, and classification for visual perception used in interactions with human beings. We propose total human visual tracking by long-term memory, k-means, self-organizing map, and a fuzzy controller is used for movement output. Experimental results show that the partner robot can conduct the human visual tracking

FMX (EEPIS FACIAL EXPRESSION MECHANISM EXPERIMENT): PENGENALAN EKSPRESI WAJAH MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION

In the near future, it is expected that the robot can interact with humans. Communication itself has many varieties. Not only from word to word, but body language also be the medium. One of them is using facial expressions. Facial expression in human communication is always used to show human emotions. Whether it is happy, sad, angry, shocked, disappointed, or even relaxed? This final project focused on how to make robots that only consist of head, so it could make a variety facial expression like human beings. This Face Humanoid Robot divided into several subsystems. There are image processing subsystem, hardware subsystem and subsystem of controllers. In image processing subsystem, webcam is used for image data acquisition processed by a computer. This process needs Microsoft Visual C compiler for programming that has been installed with the functions of the Open Source Computer Vision Library (OpenCV). Image processing subsystem is used for recognizing human facial expressions. With image processing, it can be seen the pattern of an object. Backpropagation Neural Network is useful to recognize the object pattern. Subsystem hardware is a Humanoid Robot Face. Subsystem controller is a single microcontroller ATMega128 and a camera that can capture images at a distance of 50 to 120 cm. The process of running the robot is as follows. Images captured by a camera webcam. From the images that have been processed with image processing by a computer, human facial expression is obtained. Data results are sent to the subsystem controller via serial communications. Microcontroller subsystem hardware then ordered to make that facial expression. Result of this final project is all of the subsystems can be integrated to make the robot that can respond the form of human expression. The method used is simple but looks quite capable of recognizing human facial expression. Keyword: OpenCV, Neural Network BackPropagation, Humanoid Robo

Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm

This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm

Multiple Human Face Detection Based on Local Genetic Algorithm Clustering Using Mobile iPhone Platform

This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and multiple human face detection method based on local genetic algorithm clustering. Finally, we show experimental results of the multiple human face detection using mobile platform iPhone platform to discuss the effectiveness of our proposed method

KENDALI VISUAL DUAL ARM ROBOT MENGGUNAKAN PENDEKATAN CENTER OF GRAVITY

The robot arm is very popular in the world of robotics for the future. Robot arm has several different kinds of functions. Where the robot arm in addition to functioning as a human arm, also serves as a tool in the industry, robot manipulators. In this final project will be made a Dual Arm Robot with ten degrees of freedom where there are five degrees of freedom in each arm. Dual Arm Robot Control requires that appropriate controls so that the movement of Dual Arm Robot move well and achieve the expected goals, as well as providing a bit error in the system. Therefore Control of Dual Robot Arm using web cameras can produce X and Y axis position on the detection of color. Control of Dual Robot Arm using a web camera when the data already obtained form the midpoint of the X and Y axes of the color detection using OpenCV, Dual Arm Robot to follow the movement the position of the colored object detection. Keywords: Dual Arm Robot, Web Camera, and OpenC

Flow Direction Prediction of Ball Movement for Humanoid Robot Soccer Goalkeeper

In Robocup, The most important sensor on humanoid robot soccer is a camera. The camera serves as the eyes of the robot as well as in humans. This camera is used by the robot to detect the ball. The task of Humanoid Robot Soccer as a Goalkeeper is to keep the goal and must be able to block a ball that came directly to the goal from many directions so the ball is not got into the goal. As already been known, the results of the camera image captured is always later than the pictures that has been taken, moreover if the shooting process is added with image processing such as adjusting contrast, brightness, etc. then the delay in image capture will be even greater. This causes late in reaction to the Humanoid Robot Soccer Goalkeeper when it’s blocking the ball that coming from any direction. Therefore we need a computer vision technique to estimate the direction of movement of the ball so there is no delay in reaction to the robot in blocking the ball. In this paper is discussed the flow direction prediction of the ball movement for Humanoid Robot Soccer Goalkeeper. The processing of ball movement prediction is obtained by comparing the previous ball data detection and the latest data detection to get the direction of the ball movement. This robot is a second generation of humanoid robot called EFuRIO soccer (Fußball EEPIS Robot IO)

Coil Gun Turret Control Using A Camera

ABSTRACT --- A conventional weapon usually by pointing to the target aimed by using hands. It is considered less effective and efficient in terms of military service because of spending lots of time to chase the target. So needed a tool to move the weapon automatically. This final project present about object tracking in a weapon and it’s turret, that will be controlled by camera. The camera is used to detect moving targets based on a particular color. In a image sequence consisting of many different objects, accompanied by a different background, this system will be able to distinguish between the target or not. Camera detection is done by taking moving images with color composition that has been determined. Then, The image resolution is resized of the smallest of camera’s resolutions, that is 320x240. Smaller image size are intended for the system’s working to be faster. Capturing image process is use segmentation object process in digital image processing which aims to separate the object region with background. The weapon that will be used, have two degrees of freedom. Maximum 360 degrees rotation in x axis, and maximum 90 degrees in y axis. Both of them using brushed DC motor. At the direction of the y- axis motion required a gear for transmitting power between motor shaft and the shaft, so the shaft is not directly connected to the motor and no distortion. Turret have been designed had four buffers as a solid foundation to bear the entire load. Communication between the camera and weapons carried out by using the cable. Turret will be controlled using the PD control which is expected to reach a position with a quick reference. Key Words: Object tracking, Digital Image Processing, Image sequence, PD (Proposional Deravative) Contro

rancang bangun robot pantilt: pendeteksian posisi kepala manusia menggunakan algoritma genetika

ABSTRAK Pada saat ini, banyak sekali metode pendeteksian kepala manusia. Salah satunya adalah dengan menggunakan metode algoritma genetika. Pada proyek akhir ini dikembangkan untuk tidak mendeteksi saja, tetapi koordinat yang dihasilkan nantinya digunakan untuk menggerakkan robot sehingga robot tersebut dapat mengikuti pergerakan kepala manusia. Pada proyek akhir ini, pendeteksian posisi kepala manusia ini diawali dengan merubah warna RGB menjadi warna hitam, coklat dan putih,. Proses ini dinamakan proses deteksi kulit. Setelah selesai, maka dilakukan proses GA untuk mencari posisi dari kepala manusia. Setelah ditemukan, maka komputer akan mengirim data berupa posisi x, y, lebar dan tinggi dari kepala manusia ke robot secara serial. Parameter tersebut akan dirubah sedemikian rupa sehingga robot dapat mengikuti pergerakan kepala manusia. Pada hardware robot otu sendiri digunakan 2 buah motor servo dan microcontroller ATMEGA16 dan komunikasi yang digunakan dengan komputer adalah komunikasi serial. Kata kunci: algoritma genetika, pendeteksian posisi kepala manusia