In this research, a prosthetic robot hand that features a lever-based finger movement mechanism is proposed as the new approach to the T-FLoW 3.0 humanoid robot’s hand development. The proposed approach performs both grasping and releasing movements by pushing or pulling the finger-attached lever. The lever is pushed or pulled by micro-servo, which uses a stiff bar to transfer the force from the servo-horn to the finger’s lever. Our prosthetic robot hand is equipped with six joints, six SG92R micro-servos as actuators, and six force-sensitive resistors (FSR) as grasping feedback. 3D printing manufacturing technology is utilized to give the hand a realistic appearance, and PLA filament material is used in the manufacturing process to provide low-cost, lightweight, and easy maintenance. Static structural analysis simulation result lead to the conclusion that our prosthetic robot hand could sustain a load of around 30N. With the lever-based finger movement mechanism, the proposed approach is expected to overcome the mechanical slip issues from finger movements, which are often experienced in the old approach of the T-FLoW 3.0 humanoid robot’s hand development.

This work presents a fast and simple learning algorithm for humanoid robot walking gait cases. The standard method of reinforcement learning takes too much time to learn a stable walking gait. Thus, we propose a rule-based learning method that has never been used in this kind of walking gait learning case. We implement our method in a simplified TFLoW humanoid robot model in simulation software CoppeliaSim. The result shows by using our proposed method, T-FLoW humanoid robot can walk for 200 steps after taking the learning process for about 800 episodes and has a better walking performance than the classical pattern generation for planning a walking gait motion.

The manipulator robot on the humanoid robot has functioned as an arm to grasp objects. The end-effector position of the robot is must first be known to perform the grasping task. Therefore, using the kinematics solution to find the robot end-effector position in the Cartesian space. This research paper presents the inverse kinematics of the 7-DOF T-FLoW humanoid robot manipulator using the Improved Damped Least Squares method with joint limits to avoid mechanical limitations. Forward Kinematics with the Homogeneous Transformation Matrix is used in the solution to find the current position of the end-effector in the Cartesian space. This research using the DLS method because it can avoid kinematic singularities and performs better than pseudoinverse based formulations. The experiment results show that the improved solution is more robust in performing joint limitation with a success rate of 100% and generating more natural motion than the original DLS.

Remotely operated vehicle (ROV) plays an important role in the exploration of underwater objects for observation of marine life, oil and gas exploration and rescue. In underwater diving, a variety of factors can influence the movements carried out by ROV such as water flow, water waves, water pressure, etc. Control of balance and depth in the ROV are important factor in carrying out various missions ROV found it in the form of water flow and water waves. PID controller is still ineffective due to the nonlinear nature of the ROV and therefore this paper proposes to add a Fuzzy logic controller to deal with the nonlinearity in the ROV. With a combination of PID and Fuzzy controller, the ROV is able to balance while making the diving and maneuvering moves despite receiving interference in the water. Using the proposed controller, the ROV is able to respond well with respect to disturbances in attitudes and depth motion control scenarios.

The safety and comfort of a vehicle is very important when driving. One of the security and convenience that can be achieved is to maintain the stable condition of the vehicle when it turns. For this reason a prototype was designed with a control method to control the condition of turning a vehicle. In this paper electronic device system is designed to control turn conditions using the fuzzy logic method, where fuzzy logic processes data error yaw rates and turning angles obtained from the IMU sensor. The system was applied to a three-wheeled vehicle model with a two-wheeled steering system. Based on testing with variations in turning angles and a fixed speed of 20 km / h, the largest yaw rate occurs at a 30 degree turning angle which is 0.2355 rad / s and after being given control of the yaw rate value to 0.03901 rad / s a yaw rate value reduction of 1 , 5943 rad / s. Testing with variations in vehicle speed and fixed turn angle of 10 degrees produces a yaw rate of 0.1314 rad / s and after the control is given to 0.03892 rad / s, a reduction of 0.09248 rad / s occurs. From these tests fuzzy logic is capable of improve vehicle response by minimizing slippage that occurs when the vehicle makes a turn movement so that the three-wheeled vehicle is maintained stability and security.

Technological innovations in the marine field have developed rapidly in the last decade. One of it is the innovation in the technologies related to the underwater research and exploration. It has been driven by the need of the a flexible, reconfigurable, and reliable machine for underwater operation termed Remotely Operated Vehicle (ROV). The ROV development has opened up many opportunities in the observation and exploration of underwater environment. The most important aspect in the ROV is the flexibility of motion during the maneuver in the water. The mechanical structure and also the static and dynamic analysis is very important aspect in designing ROV related to its operation environment. This paper proposes ROV body design based on analysis characteristic hydrodynamic and buoyancy effect using CFD. The characteristics of each fluid flow affect the motion of the ROV which movement is driven by the thrusters. The hydrodynamic characteristics and buoyancy effect will be analyzed. The result will be used in the further mathematical calculations in the controller design phase.

This paper discusses about research for developing human-like robots. To make the robot walk like a human on inclined surface, it is currently being developed into two major parts, namely the mechanical system and the equilibrium motion control system. In this mechanical system, it discusses the relationship of all human parts that are modeled on each joint and link to the robot. The analysis used in modeling this mechanical system is dynamic walking and Dynamic Step for robot on the inclined surface . Whereas for motion control systems on inclined surface, Online Motion Stabilization and Online Posture Stabilization analysis is used. Planning balance movements is used for each joint to make the robot walk stable in inclined surface. Balance system planning is made based on input data obtained from sensor readings from the position of the robot. Then the triple inverse pendulum approach can be used which can be solved by simulating the movement from the feet and body . The Walking Simulation Result have success for dt = 20 ms. It means that the distances and joint values won`t be updated any faster that every 20 ms .

Walking on uneven surface with balanced manner is the key of the humanoid robot. Many essential parameters of the human walking can be captured with seven links planar biped robot. Hence, in this work kinematics, dynamic modeling and trajectory planning of a seven links planar biped robot with six joints, walking on stair with different level ground in the sagital plane. In this paper, the discussion is concerned to control kinematics for dynamic up and down stairs of the humanoid robot with Full-body kinematics based on Zero Moment Point (ZMP) to analyze stability of the bipedal robot. We define that stair configuration is already known. Dynamic of stair climbing is more unstable than dynamic walking on the ground because it needs an additional vertical motion and has different step length of walking. On this kinematics, center of the robot position of mass is adjusted by the upper body of the robot.Trajectory planning based on the behaviour human walking. The validity of the proposed method is confirmed by simulation, using geometry analysis equation and a uniques strategy for ascending and descending stairs. Furthermore Full-body kinematics for analysis has a good result on the simulation experiment with accuracy 100 percent. Position vector from Full-body kinematics will be used to complete the dynamic system model of T-Flow humanoid robot.

The important thing that should be consist on delivery drone is landing speed and dropping object with smoothly. For the two cables and two motor pulley system, balancing control are use to balancing the cable that carrying the gripper, while the speed on landing system are controlled. The main advantage to build the system are using balancing PID controller with cable length input and speed control to makes the pulley automatically landing and pulling the object. The object can be land smoothly with speed control, and the cable will be in stable parallel position when the pulley are rotating with balancing control. To detect the object height, we use the proximity sensor and calibrating with rotary encoder. In this paper, we have installed the speed and balancing control on the two cables pulley-gripper system for delivery drone. The final result, the pulley can be land the box smoothly from 4 - 5 meters distance and stable the cable distance with error less than 2 cm respectively.

The main parameters to generate and calculate dynamics system models is position of each joint from its origin position. This paper describes about full body calculation position of each joint based on system design of "T-FLoW" humanoid robot using forward kinematics (FK) analysis. The FK analysis is used to transform the rotational value (degree) of each joint into position vector (cartesian space) of each joint. FK analysis in each joint is combined step by step from its origin until reach the last joint number (end of effector - EoE). The result from this combination is position vector of each joint from its origin. From the result shows the full body FK analysis with homogenous transformation method is represent the real pose of T-FLoW humanoid robot. This full body joint position vector is used to complete the dynamics system model calculation of T-FLoW humanoid robot.

T-FLoW (Teen FLoW) is one of the teen size humanoid robots developed by ER2C (EEPIS Robotics Research Center) laboratory. There are various problems that occur when the robot performs dynamic locomotion, one of them is the overloading received on every actuator thus causing the asynchronous locomotion of the robot. In this paper, the goal to be achieved is making a vertical jumping mechanism of the robot to become more synchronized. Therefore, multivariable control will be used as a method to distribute overload for each actuator on the same joint so that actuator can be more synchronized. Finally, multivariable control has a positive effect in which the success rate of the vertical jumping mechanism is about 80%, and the robot managed to hover on the air for about 7-9 ms.

This paper discusses about analysis the mechanical and forward kinematic of Prosthetic Robot Hand for T-FLoW 3.0, because it is the basic thing in building Prosthetic Robot Hand. This Prosthetic Robot Hand has 15 DoF which represented by 6 motors as the actuator. Mechanism which used for actuating each finger is by twisting the cable. The material used is PLA with 3D printed way for getting mass as light as possible and also easy to install. Mechanical analysis in this paper is to discuss the strength of the finger structure using the static structural analysis method. The kinematics that will be discussed in this paper is Forward Kinematic (FK). In mechanism design, FK is used to find constraint, calculate position vector of End of Efector (EoE). But, this paper only discusses the FK used to be looking for the constraint, it means that how maximum is the point that can be reached by finger with maximum degree of each joint of finger.

Research on T-FloW humanoid robot in EEPIS Robotics Research Center (ER2C) has entered the third generation. Previously, the robot already had primitive motions to mimic 'human-like' walking capabilities by making use of an open loop motion controller. Yet, a feedback control system is required in order to take corrective measures due to walking parameter variations. In this current stage of the work, the development is aimed to provide the robot with adequate number of sensors to detect the movement of the robot. This paper describes the utilisation of the sensors in our robot to analyze and detect its condition when the robot begins to fall down. An inertial measurement unit (IMU) sensor consists of 3-axes accelerometer and 3-axes gyrometer was in use. The analysis was done under V-REP simulation software which is equipped with Vortex dynamic engine. The simulation showed the detected characteristic of the walking parameters of the robot just before it begins to fall down. This simulation result is paramount for further T-FloW walking controller development.

Recently, innovation in the technologies has been driven by the need of a flexible, reconfigurable, and reliable machine for under water operation termed as Remotely Operated Vehicle (ROV). Development of the ROV has opened many opportunities for exploring and make the better understanding of our underwater environment. Degree of freedom is an important aspect in ROV in enabling the flexibility of the movement during its maneuver underwater. This paper proposes the design of a remotely operated underwater vehicle (ROV) with 5 DOF using multi-thrusters configuration. Since the horizontal movement of the proposed ROV needs more flexibility, thus in this design 4 thrusters are situated to handle movement of the ROV in the horizontal plane, and the remaining 2 thrusters are handling the vertical plane movement. To get better understanding about the underwater environment, several sensors are needed to be deployed in the ROV. The proposed ROV design already accommodates the sensors planned to be installed in the vehicle when designing the mechanical model of the ROV. Finally, the proposed mechanical design and the dynamic model of the ROV have been presented for its future development process.

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.