Zero moment point (ZMP) is a mathematical formulation to find a point that causes equilibrium of action and reaction momentum (momentum equal to zero). ZMP can be approached using Single Linear Inverted Pendulum Model (SLIPM). In this paper will explain the modeling of walking trajectory based on the mathematical formulation in SLIPM on the “T-FLoW” humanoid robot. In the SLIPM mathematical equation has 2 main components which are position vector of Center of Mass (CoM) and Acceleration (Linear) of CoM. From these two main compositions, there will be two types of walking trajectory models to be used and implemented in the “T-FLoW” humanoid robot. The first walking locomotion analysis is walking trajectory model the position vector of CoM is dominant. The second walking locomotion analysis is walking trajectory model when the acceleration in CoM is dominant. The results of these two walking trajectory models are for modeling and establishing a control system for robot stability based on dynamic characteristic on both walking trajectory models in the next research.

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.

The ability of a robot to be able to change the shape is not something impossible to do. This research introduce such changeable shape robot named PodRacle, which has two basic forms; a form of a six-legged robot and a form of spherical robot. Combining the two basic forms, the PodRacle robot can result in increasing efficiency of a six-legged robot motion on a flat surface by means of rolling movement. The spherical robot shape can roll on a flat surface, then has a rolling speed that is faster than the walking legged robot. In order to implement the rolling movement, a prototype of robot mechanism is designed and its kinematics and dynamics equations are derived. The effectiveness of the robot and the equations are tested through experiments.

Basically Biped robots had to walk like human, to generate this system we use basic approach for analysis human walking behavior. Human walking behavior can be analyzed using Zero Moment Point (ZMP). Zero Moment Point (ZMP) is a zero point which total of gravity forces and horizontal inertia equal 0 (Zero). This paper describes about early step to made basic walking trajectory for FLoW robot. To determine the best Zero Moment Point (ZMP) reference, it can use the comparison of distance and time. From this comparison, the walking characteristic of FLoW robot was found and the best Zero Moment Point (ZMP) trajectory also founded. The basic walking trajectory will generate based on the best Zero Moment Point (ZMP) trajectory as a reference. In the FLoW robot, the best Zero Moment Point Trajectory was founded at distance of 14cm with difference of Zero Moment Point Reference and Real Zero Moment Point equal 0.012cm.

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.

Robotics has been widely used in education as a learning tool to attract and motivate students in performing laboratory experiments within the context of mechatronics, electronics, microcomputer, and control. In this paper we propose an implementation of cascaded PID control algorithm for line follower balancing robot. The algorithm is implemented on ADROIT V1 education robot kits. The robot should be able to follow the trajectory given by the circular guideline while maintaining its balance condition. The controller also designed to control the speed of robot movement while tracking the line. To obtain this purpose, there are three controllers that is used in the same time; balancing controller, speed controller and the line following controller. Those three controllers are cascaded to control the movement of the robot that uses two motors as its actuator. From the experiment, the proposed cascaded PID controller shows an acceptable performance for the robot to maintain its balance position while following the circular line with the given speed setpoint.

Balancing robot which is proposed in this paper is a robot that relies on two wheels in the process of movement. Unlike the other mobile robot which is mechanically stable in its standing position, balancing robot need a balancing control which requires an angle value to be used as tilt feedback. The balancing control will control the robot, so it can maintain its standing position. Beside the balancing control itself, the movement of balancing robot needs its own control in order to control the movement while keeping the robot balanced. Both controllers will be combined since will both of them control the same wheel as the actuator. In this paper we proposed a cascaded PID control algorithm to combine the balancing and movement or distance controller. The movement of the robot is controlled using a distance controller that use rotary encoder sensor to measure its traveled distance. The experiment shows that the robot is able to climb up on 30 degree sloping board. By cascading the distance control to the balancing control, the robot is able to move forward, turning, and reach the desired position by calculating the body's tilt angle.

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.

Balancing robot is a robot that relies on two wheels in the process of movement. Basically, to be able to remain standing balanced, the control requires an angle value to be used as tilt set-point. That angle value is a balance point of the robot itself which is the robot's center of gravity. Generally, to find the correct balance point, requires manual measurement or through trial and error, depends on the robot's mechanical design. However, when the robot is at balance state and its balance point changes because of the mechanical moving parts or bringing a payload, the robot will move towards the heaviest side and then fall. In this research, a cascade PID control system is developed for balancing robot to keep it balanced without changing the set-point even if the balance point changes. Two parameter is used as feedback for error variable, angle and distance error. When the robot is about to fall, distance taken from the starting position will be calculated and used to correct angle error so that the robot will still balance without changing the set-point but manipulating the control's error value. Based on the research that has been done, payload that can be brought by the robot is up to 350 grams.