Penalty Kick of a Humanoid Robot by a Neural-Network-Based Active Embedded Vision System

[[abstract]]This paper realizes the humanoid robotic system to execute the penalty kick (PK) of the soccer game. The proposed system includes the following three subsystems: a humanoid robot (HR) with 22 degree-of-freedom, a soccer with different colors, and a soccer gate. In the beginning, the HR scans the soccer field to find the gate and the soccer, which are randomly distributed in a specific region in the front of the gate. If a command for the PK of the soccer with specific color is assigned, the HR will be navigated by an active embedded vision system (AEVS). After the HR reaches a planned position and posture, the PK of the HR will be executed. Two key important techniques are developed and integrated into the corresponding task. One is the modeling using multilayer neural network (MNN) for different view angles, the other is the visual navigation strategy for the PK of the HR. In addition, the error sensitivities in the pan and tilt directions of these four visible regions are analyzed and compared. The proposed strategy of the visual navigation includes the following two parts: (i) the switched visible regions are designed to navigate the HR to the planned position, and (ii) the posture revision of the HR in the neighborhood of the soccer in order to execute the PK. Finally, a sequence of experiments for the PK of the HR confirm the effectiveness and efficiency of the propose methodology.[[conferencetype]]國際[[conferencelocation]]Taipei, Taiwa

Параллельный алгоритм метода сетевого оператора в задаче многокритериального синтеза системы стабилизации двуногого шагающего робота

It is considered a problem of two-tired inverted pendulum stabilization. Pareto-region for two functional is found by using network operator and parallel realization of genetic algorithm. Every point of that region gives a solution - state vector feedback control function. It is presented simulation results for one point of Pareto-region.Рассматривается задача стабилизации углового положения двуногого шагающего робота, описываемого моделью двухзвеннгого обратного маятника. Управление ищется в виде обратной связи по вектору состояния. С помощью параллельного генетического алгоритма, использующего метод сетевого оператора, строится множество Парето эффективных решений на пространстве двух функционалов: время стабилизации и норма вектора состояния. В статье приводится результаты численного эксперимента

Logika Fuzzy pada Robot Inverted Pendulum Beroda Dua

AbstrakRobot inverted pendulum  beroda dua (IPBD) merupakan sistem yang tidak stabil dan bersifat non-linear. Motor DC sebagai penggerak robot yang terletak pada masing-masing roda kiri dan kanan memberikan variabel gaya untuk mempertahankan kestabilan robot. Oleh karena itu diperlukan suatu kendali yang dapat menjaga keseimbangan dari robot. Makalah ini memaparkan kendali logika fuzzy dalam hal pengendali keseimbangan robot. Pada perancangan robot ini, penulis menggunakan senor inertia measurement unit (IMU) versi MPU 6050 sebagai sensor pendeteksi keseimbangan robot. Nilai setpoint sudut robot yang diberikan adalah sudut elevasi robot terhadap sumbu horizontal atau pada sumbu pitch. Selanjutnya, nilai keluaran sensor IMU dibandingkan dengan setpoint. Lebih lanjut, nilai kesalahan (error) dan nilai perubahan kesalahan (delta errror) yang dihasilkan akan digunakan sebagai masukan logika fuzzy. Hubungan relasi masukan fuzzy diselesaikan dengan aturan Mamdani. Keluaran dari logika fuzzy diselesaikan dengan perhitungan weight average (WA). Hasil keluaran logika fuzzy berupa nilai putaran motor kiri dan kanan yang dikendalikan dengan cara mengatur lebar pulsa sinyal pulse with modulation (PWM). Dari hasil pengujian diperoleh bahwa kendali logika fuzzy yang diaplikasikan pada robot IPBD dapat menjaga keseimbangan robot dengan osilasi pada sudut -2 hingga 2 derajat.Kata kunci: Logika Fuzzy, Inverted Pendulum, IMU  AbstractInverted robot pendulum two (IPBD) is an unstable system that is naturally and non-linear. The DC motor as a robot drive located on each of the left and right wheels provides a force variable to maintain the robot's stability. Therefore we need a control that can maintain the balance of the robot. This paper presents fuzzy logic control in terms of robot balance control. In designing this robot, the author uses inertia measurement unit senator (IMU) MPU 6050 version as a robot balance detection sensor. The given set of corner robot values is the robot's elevation angle to the horizontal axis or on the pitch axis. Furthermore, the value of the IMU sensor output is compared with the setpoint. Furthermore, the error value and the resulting error change value (delta errror) will be used as fuzzy logic input. The relation of fuzzy input relation is solved with Mamdani rule. The output of fuzzy logic is solved by calculating the weight average (WA). The result of fuzzy logic output is left and right motor rotation controlled by adjusting pulse signal of pulse with modulation (PWM). The experiment results obtained that fuzzy logic control applied to the robot IPBD can maintain the robot balance by having oscillations at an angle of -2 to 2 degrees.Keywords: Fuzzy Logic, Inverted Pendulum, IMU

Humanoid Balancing Behavior Featured by Underactuated Foot Motion

A novel control synthesis is proposed for humanoids to demonstrate unique foot-tilting behaviors that are comparable to humans in balance recovery. Our study of model-based behaviors explains the underlying mechanism and the significance of foot tilting well. Our main algorithms are composed of impedance control at the center of mass, virtual stoppers that prevent overtilting of the feet, and postural control for the torso. The proof of concept focuses on the sagittal scenario and the proposed control is effective to produce human-like balancing behaviors characterized by active foot tilting. The successful replication of this behavior on a real humanoid proves the feasibility of deliberately controlled underactuation. The experimental validation was rigorously performed, and the data from the submodules and the entire control were presented and analyzed