EEPIS Robotics Research Center (ER2C) has been built a humanoid robot and the name is “T-FLoW” humanoid robot. This Humanoid robot is used to solve the problem in the direct pass during moving ball in the technical challenge RoboCup Competition. This paper will explain about the implementation of direct pass strategy during moving the ball into “T-FloW” humanoid robot. Direct pass strategy is splitting into 3 step. The first step is how to calculate and predict the position, speed, and acceleration during ball movement by using camera vision strategy. The second step is how to doing kick motion when the ball is approaching the foot based on the position, speed, and acceleration from the first step. The third step is the main strategy to combine the first and second step. Based on the experiment result, the success to pass the ball is 80%. The kicking locomotion is not fast enough. Perhaps in next research, the kicking locomotion will be made with fully efficient and faster than before.

In Indonesia research on Humanoid is limited to Kid-Size only, while for Teen-Size is still rare. This encourages teams and researchers to create a Teen-size humanoid called “FLoW”. To perform flexible movements like humans is not easy to do robot. Some of the problems that arise and become the focus of this research is to organize the movement of the robot in order to move, synchronize the movement in every parts of the humanoid robot, mathematical modeling and simulation of robot movement when standing. Taking into account the total kinematics of the humanoid robot, the movement of the robot can be overcome. With forwardinvers kinematics, researchers can determine a movement of the robot by controlling the motor part at a certain point. This section is an actuator of the robot. To do the modeling and simulation using D-H parameter and V-REP simulation software aid. Then for forward-invers kinematics can be implemented on the PID algorithm with the output of the speed on the motor that can form an angle on the motor to drive the robot. The expected result of this research is Humanoid Robot can move to follow the trajectory that has been determined. Making Humanoid Teen-Size robot is expected to increase research on Humanoid Teen-Size in Indonesia. In addition, this FLoW robot can be a supporting material and reference for the development of Humanoid Teen-Size next.

A map of a location made of a human mindset is made briefly by recording a situation already seen in memory. Human behavior is easy to do without a lot of thinking. The process is observed more deeply that when the human mind does not do the behavior in turn like looking at the whole room then just remember, but humans can see at once remember simultaneously. Bringing human behavior into a computer system is done by mimicking how humans look. Looking at using two cameras in combination with the sensor structure. To process the device requires a large computing. Be a challenge for Portable CPU that have lower capabilities than high end CPU. For that humanoid robot can perform tasks simultaneously and can accelerate, especially in image processing using portable CPU required a method that makes the processing into realtime. Achieving realtime with data from previous researcher can not be done with sequential algorithms when the complexity is high. After analyzing the problem was built parallel camera mapping process using multithread algorithm. This paper aims to accelerate the basic process of making the map so as to achieve realtime. This research aims to change the previous process that runs sequentially into a parallel system using multithread algorithm. The results of each process are compared between parallel systems with multithread algorithm and do not use parallel systems. There is a change in speed between the two processes. Further research is concerned with improving the mapping environment results when visualized.

In this paper, present a vehicle actuator response using a hybrid control and make the vehicle has electronic stability program. Electronic Stability Program (ESP) is an automatic stability control systems on vehicles. Where the system works to avoid understeer and oversteer conditions when the vehicle is turning. Understeer is a condition where the vehicle more difficult to turn, this is because the front wheels tend to slip so that the vehicle will be more difficult to turn. Oversteer is a condition in which the vehicle is easier to turn, this is because the rear wheel tends to slip so that the vehicle will be easier to turn. Understeer and oversteer conditions often occur when turn or when a vehicle suddenly turn to avoid another vehicle. The control system designed for controlling vehicle is Fuzzy and PID-AFC. Fuzzy is for the high level control and PID-AFC is for low level control. Fuzzy processed a wheel speed, steering angle and IMU data. And PID-AFC receive set points value for wheel deceleration and acceleration from Fuzzy output. PID-AFC control the speed of left and right rear wheels when slip occurs. With this control the vehicle is expected to have a fast response and resistant to slip that happened. The test has successfully compared between PID and PID - AFC when running the motor. Without control, steady state error reaches 27.1%. When using PID steady state error only 1.7% with oscillation 12 times, when there is disturbance PID takes 1.4 seconds to return to setpoint. When using PID-AFC steady state error is only 1.7% with oscillation only 5 times and when any disturbance PID-AFC takes only 1.1 seconds to return to setpoint.

Unicycle mobile robot is wheeled mobile robot that can stand and move around using one wheel. It has attached a lot of researchers to conduct studies about the system, particularly in the design of the system mechanisms and the control strategies. Unlike two wheel balancing mobile robot which mechanically stable on one side, unicycle mobile robot requires additional mechanisms to keep balancing robot on all sides. By assuming that both roll dynamics and pitch dynamics are decoupled, so the balancing mechanisms can be designed separately. The reaction wheel is used for obtaining balancing on the roll angle by rotating the disc to generate momentum. While the wheeled robot is used for obtaining balancing on the pitch angle by rotating wheel to move forward or backward. A PID controller is used as balancing control which will control the rotation motor on the reaction disc and wheel based on the pitch and roll feedback from the sensor. By adding the speed controller to the pitch control, the system will compensate automatically for perfectly center of gravity on the robot. Finally, the unicycle robot will be able to balance on pitch angle and roll angle. Based on simulation result validates that robot can balance using PID controller, while based on balancing pitch experiment result, robot can achieve balancing with maximum inclination about ±23 degree on pitch angle and ±3.5 degree on roll angle with steady state error 0.1 degree.

Pens-Wheel is an electric vehicle which uses one wheel that able to balance itself so the rider does not fall forward or backward while riding it. This vehicle uses one brushless DC motor as actuator which capable to rotate in both directions symmetrically. The vehicle uses a combination of accelerometer and gyroscope contained in IMU (Inertial Measurement Unit) for balancing sensor. The controlled motion on the vehicle occurs only on the x-axis (pitch angle), in forward and backward directions. The PID (Proportional Integral Derivative) control algorithm is used to maintain the balance and movement of the vehicle. From the simulation and application in the real vehicle, the use of PID control is capable driving the vehicle in maintaining the balance condition within ±10° tilt angle boundary on flat surface, bumpy road, and inclining road up to 15° slope.

Many electrical vehicles have been developed recently, and one of them is the vehicle type with the self-balancing capability. Portability also one of issue related to the development of electric vehicles. This paper presents one wheeled self-balancing electric vehicle namely PENS-Wheel. Since it only consists of one motor as its actuator, it becomes more portable than any other self-balancing vehicle types. This paper discusses on the implementation of Kalman filter for filtering the tilt sensor used by the self-balancing controller, mechanical design, and fabrication of the vehicle. The vehicle is designed based on the principle of the inverted pendulum by utilizing motor's torque on the wheel to maintain its upright position. The sensor system uses IMU which combine accelerometer and gyroscope data to get the accurate pitch angle of the vehicle. The paper presents the effects of Kalman filter parameters including noise variance of the accelerometer, noise variance of the gyroscope, and the measurement noise to the response of the sensor output. Finally, we present the result of the proposed filter and compare it with proprietary filter algorithm from InvenSense, Inc. running on Digital Motion Processor (DMP) inside the MPU6050 chip. The result of the filter algorithm implemented in the vehicle shows that it is capable in delivering comparable performance with the proprietary one.

The Inverted pendulum platform is an example of classic unstable control system. Even though the system has been fairly tested and documented, it still draws attention of many researchers due to its application in unicycle robot. In the unicycle robot, there are problems that arise control strategy in the reading position of the robot tilt. This paper proposes to use the Kalman Filter Estimation angle for data processing Inertial Measurement Unit (IMU) to obtain estimates of the robot tilt position. In the previous study also found problems when using only one relatively low speed IMU sensor obstacles that the response given by the sensor. This paper uses two IMU sensor readings to speedup the response of the sensor and get accurate data during a shorter period. The proposed algorithm uses a new sensor placement strategy on a rigid body robot, with a reading sensor in interleaved mode. Kalman Filter algorithm incorporating placement constraints to achieve the estimated position of the robot tilt angle accurately. The results show synchronization time sampling of the two Inertial Measurement Unit (IMU) sensor improves the response and a twice faster in estimating the position of the robot tilt compared to the use of one sensor. Merging time sampling 2 sensors can be applied on a unicycle robot in order to have a quick response to the reading of the tilt position of the robot.

One of the focus in humanoid robot research is head motion and mechanism, yet it still has a limited pan and tilt motion to represent a real human head movement. This study is working on kinematic analysis of an earlier proposed 7 DOF manipulator system that can closely mimics a human head motion capability. The concentration of the work is to design a head mechanism and to develop a composite kinematics model of the head motions which includes the eyes, the head itself and the neck. Such a model was also simulated using the dynamic V-REP application to verify its movements capabilities and to compare the kinematics analysis. Stages will be discussed in more detail in the chapter on testing. The results showed transformation biggest difference is 2.3340% error that occurred on 3 DOF motion contained in the head manipulator.

In this paper we described a model and a simulation of walking pattern of FLoW bipedal robot. This kinematics design combined four-bar linkages and translational actuators. Inverse kinematics problem is solved by using trigonometry approach. While walking pattern is made of a superposition between linear and sinusoidal function. The equations is simple and does not required long computation time. So it is efficient to maximize overall process on robot. The stability of the robot is controlled by CoM point which is calculated by multiplying the mass and position of the robot element and keeping it remain in the robot support polygon.

In this paper, we described a model and a simulation of forward and inverse kinematics of a parallel link leg performing a predefined hula-hoop motion of FLoW, a bipedal humanoid robot. This lower body leg having 12 DOF links and joins configuration were described using D-H parameters. It was assumed that the motion is slow enough. Thus, by keeping the centre of mass projection to the floor to be always inside the support polygon of the robot the system can be regarded as stable.

Humanoid robot has become popular research platforms in robotics and artificial intelligence. Humanoid robot can perform complex motions, including the balancing, walking, and kicking skills required in the RoboCup robot soccer competition. The humanoid robot from the Electronic Engineering Polytechnic Institute of Surabaya (EEPIS) research center named FLoW. This paper discusses about the FLoW head mechanism. Flow head mechanism is split into three parts there are eye mechanism, head mechanism and neck mechanism. The forward kinematic from FLoW head mechanism is obtained to generate workspace of motion. The FloW head workspace of motion is compared to human workspace of motion. From the experiment the motion of FLoW head already approached human easy head motion.