Real-time Coordinate Estimation for Self-Localization of the Humanoid Robot Soccer BarelangFC

In implementation, of the humanoid robot soccer consists of more than three robots when played soccer on the field. All the robots needed to be played the soccer as human done such as seeking, chasing, dribbling and kicking the ball. To do all of these commands, it is required a real-time localization system so that each robot will understand not only the robot position itself but also the other robots and even the object on the field’s environment. However, in real-time implementation and due to the limited ability of the robot computation, it is necessary to determine a method which has fast computation and able to save much memory. Therefore, in this paper we presented a real-time localization implementation method using the odometry and Monte Carlo Localization (MCL) method. In order to verify the performance of this method, some experiment has been carried out in real-time application. From the experimental result, the proposed method able to estimate the coordinate of each robot position in X and Y position on the field.Dalam implementasinya, robot humanoid soccer terdiri lebih dari tiga robot di lapangan ketika sedang bermain bola. Semua robot diharapkan dapat memainkan sepak bola seperti manusia seperti mencari, mengejar, menggiring bola dan menendang bola. Untuk melakukan semua perintah tersebut, diperlukan sistem lokalisasi real-time sehingga setiap robot tidak hanya memahami posisi robotnya sendiri tetapi juga robot-robot lain bahkan objek yang berada di sekitar lapangan. Namun dalam implementasi real-time dan karena keterbatasan kemampuan komputasi robot, diperlukan suatu metode komputasi yang cepat dan mampu menghemat banyak memori. Oleh karena itu, dalam makalah ini menyajikan metode implementasi lokalisasi real-time dengan menggunakan metode odometry and Monte Carlo Localization (MCL). Untuk memverifikasi kinerja metode ini, beberapa percobaan telah dilakukan dalam aplikasi real-time. Dari hasil percobaan, metode yang diusulkan mampu mengestimasi koordinat posisi robot pada posisi X dan Y di lapangan ketika sedang bermain bola

Humanoid Localization on Robocup Field using Corner Intersection and Geometric Distance Estimation

In the humanoid competition field, identifying landmarks for localizing robots in a dynamic environment is of crucial importance. By convention, state-of-the-art humanoid vision systems rely on poles located outside the middle of the field as an indicator for generating landmarks. However, in compliance with the recent rules of Robocup, the middle pole has been discarded to deliberately provide less prior information for the humanoid vision system to strategize its winning tactics on the field. Previous localization method used middle poles as a landmark. Therefore, robot localization tasks should apply accurate corner and distance detection simultaneously to locate the positions of goalposts. State-of-the-art corner detection algorithms such as the Harris corner and mean projection transformation are excessively sensitive to image noise and suffer from high processing times. Moreover, despite their prevalence in robot motor log and fish-eye lens calibration for humanoid localization, current distance estimation techniques nonetheless remain highly dependent on multiple poles as vision landmarks, apart from being prone to huge localization errors. Thus, we propose a novel localization method consisting of a proposed corner extraction algorithm, namely, the contour intersection algorithm (CIA), and a distance estimation algorithm, namely, analytic geometric estimation (AGE), for efficiently identifying salient goalposts. At first, the proposed CIA algorithm, which is based on linear contour intersection using a projection matrix, is utilized to extract corners of a goalpost after performing an adaptive binarization process. Then, these extracted corner features are fed into our proposed AGE algorithm to estimate the real-word distance using analytic geometry methods. As a result, the proposed localization vision system and the state-of-the-art method obtained approximately 3-4 and 7-23 centimeter estimation errors, respectively. This demonstrates the capability of the proposed localization algorithm to outperform other methods, which renders it more effective in indoor task localization for further actions such as attack or defense strategies

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion