This paper proposed an algorithm to evaluate hole properties automatically. The proposed algorithm analyzes probe path, reference data, measurement data, and tolerance data to evaluate hole properties based on tolerance data ISO 2768. The proposed algorithms was verified experimentally by implementing to holes evaluation software which is developed by phyton programming. A 3D product model with two different hole was designed to validate the proposed algorithm. according to the experiment result, proposed algorithm can evaluate hole properties properly with quality control sheet format as output. Then, the proposed algorithm experiment is compared with manual evaluation.

Emotion is an intense feeling that is directed at someone or something that can trigger to take an action or expression from inside or outside themself. In everyday life, it is important to be able to understand and predict the emotional state of a person. One of the emotions of a person can be known through facial expressions, but sometimes a person can manipulate it by controlling his facial expressions. Therefore, we need a system that can predict children's emotions based on changes in their body condition. In this study, two sensors were used, namely the GSR sensor and the heart rate sensor. The data obtained from each sensor is processed using a microcomputer or raspberry pi. Furthermore, the sensor output will be processed first using the Support Vector Machine (SVM) method by comparing 2 pieces of data, namely normalized data and unnormalized data based on linear, polynomial, RBF, and sigmoid kernels, so that a model will be obtained and will be used as a reference to predict 3 types of emotions namely happy, sad, and angry. From this research, the best model is obtained when using linear and sigmoid kernels with normalized data types. This is proven when using normalized data with linear and sigmoid kernels, there are only 18 incorrect data with an accuracy rate of 86.67%. This research is expected to help parents and teachers to predict emotions, especially in children.

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.

Based on computer vision technology, this research suggests an application to identify and assess a fitness practitioner’s movements. Several fitness movements such as lifting weights, squat jumps, and pull-ups that are very beneficial for health and body fitness become the main movement for body building. However, those kinds of activities may be very dangerous if done incorrectly. Based on the problem, we developed an application based on computer vision to recognize and correct the pose accuracy of fitness practitioners by using input in the form of videos that record the movements of fitness practitioners continuously. To categorize the many forms of fitness sport movements, this system uses the support vector machine (SVM) method. On the monitor screen, the classification results will be visible. The result shows that the accuracy of the system is 96.87% by using SVM with the Radial Basis Function (RBF) kernel type and can make corrections to four types of fitness movements with a testing accuracy of 90.62%.

The Unmanned Ground Vehicle (UGV) Robot Arm is needed to take samples of volcanic material, namely material samples in the form of rocks from the Volcano which will later be studied more accurately. The focus of this research is on developing a robotic arm to take samples of volcanic material mounted on the UGV. The Robot Arm consists of 3-DOF (Degree Of Freedom) equipped with a Gripper to make it easier to take samples of the material from the Top of the Volcano. The Robot Arm is placed on the upper front of UGV Robot body. The movement of the Robot Arm utilizes Rotational Motion in each DOF. The Kinematic Model of this 3-DOF robotic arm was built using the Denavit Hartenberg method. This study discusses the movement of the robot arm with 3 degrees of freedom where the robot used has one kinematic strand consisting of 3 joints with the revolute type. The angle at each joint can be determined using a method used in this research, namely forward kinematics and inverse kinematics. Experimental activity shows that the error occurs because the mechanical system is less precise rather than the method used

In this paper, the development of inspection code generator for hole cylindrical feature evaluation is reported. The development process aims to enable on-machine measurement process through Computer Aided Inspection Planning (CAIP) software. A CNC machine equipped with touch probe measuring tool is utilized in this research as an object for On-Machine Measurement (OMM) System. The hole feature as measurement object were defined from feature recognition process. An algorithm to construct inspection path planning was developed according to hole feature measurement point. Inspection path planning define touching point, retract point, clearance ratio, and retract distance from the measurement point. The inspection path planning is transformed into inspection code based on Heidenhain iTNC 30i controller code standard. The Inspection code were experimentally executed in the DMG 50U CNC Machine to validate the path planning. Renishaw touch probe were used to measure the hole feature dimension and position. The OMM result was compared to the vernier and measurement result. The experiment result shows that the proposed method has accurate equal inspection result to the vernier.

Currently, much medical personnel died because of being infected by COVID-19 and because of low personal protective facilities and the duties of medical personnel that must carry out to deliver the logistics to patients and make many contacts between the medical personnel and patients of COVID-19. Mobile robots are considered the right solution to complete this problem. With mobile robots, hospitals or the place of isolation can minimize contact between medical personnel and patients of COVID-19 by carrying out the logistic delivery task. To deliver the logistic, a mobile robot must have low-level control, and the mechanism to carry out the workpiece also have the mechanism to open the door. The mechanism to carry out the workpiece is a system to pick up and place the rack of logistics from one place to another. In this study, the low-level control was applied using a PID control with the parameter's value $\boldsymbol{k}_{\boldsymbol{p}}=500,\boldsymbol{t}_{\boldsymbol{i}}=0.001$, and $\boldsymbol{t}_{\boldsymbol{d}}=0.001$ and teleoperation to control the mobile robot manually, so the mobile robot was able to move and carry out the load with the maximum value is 13 kg also open the door. Based on the results of the tests that have been carried out, the mobile robot with the proposed low-level control and the object management system can do the delivery task to reduce contact between medical personnel and patients of COVID-19, also the mobile robot can be controlled manually.

Kontes Robot Pemadam Api Indonesia (KRPAI) is a division of the Robot Competition in Indonesia whose mission is to extinguish fires according to the rules of the Trinity College International Fire Fighting Robot Contest (TCIFFRC) in Hartford, United States. The robot is placed in one of the 4 rooms randomly and is required to find a fire then extinguish it and return to the room where the robot is placed. The ability of robots to be able to carry out missions perfectly often fails because of the disturbance of obstacles in the room, do not know the pose of the robot when outside the room. A map is very important so that the robot knows its position in space and the distance of the robot from obstacles. Therefore, the focus of this research is not only on simulations but also on direct map-making to dynamically moving four-legged robots. The odometry method on the four-legged robot is used for routing and localization algorithms in making maps. Bresenham's algorithm is implemented in the map creation process. The results of the test and analysis show that a 1 mm grid map to a 5 mm grid map can describe parabolic-shaped obstacles, and the average error cell value for corridors, door widths, and walls is 0.92 cells.

Many researchers have developed disaster robots. This paper aims to create disaster robot assistant for helping humans in handling victims of natural disasters. This preliminary research is devoted to the interface and instrumentation of the Brushless Direct Current (BLDC) motor. The focus of this paper is on programming techniques for BLDC motors based on Arduino and Digital to Analog Converter (DAC) which are open loop. The robot used to monitor areas that are declared dangerous and can cause natural disasters. In this preliminary research, an assist robot was created that runs on roads that are not intended to help humans carry several environmental monitoring devices on volcanoes. The robot carries an Unmanned Aircraft Vehicle (UAV) equipped with several sensors for monitoring environmental conditions. System testing is to provide setpoint data and take measurements of the response in the form of output voltage from the DAC and motor rotation speed (RPM). The measurement results for motor speed (RPM) is 11.915 and for DAC (volt) is 0.0169, where the data is a direct comparison of measurements to measurements from settings using Arduino.

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.

This paper develops and analyzes a set of arm and hand mechanical system of the T-FLoW Humanoid Robot, which consists of a 7 Degree of Freedom (DoF) Arm and a 6 Degree of Freedom (DoF) Hand. With Kinematic calculations, mathematical modeling of the arm can be obtained using rotational matrices and translational matrices based on the rotational frame at each joint of the robotic arm and hand. Forward Kinematic (FK) analysis requires a combination of homogeneous matrices obtained from the rotation frame of each joint and the distance of each joints. From the results of Forward Kinematic, it can be used as a robot modeling in Matlab visualization by comparing robot’s hand and arm model on V-REP so that the original pose of the arm and hand of the Humanoid T-FLoW robot can be known.

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.

Lately, during the COVID-19 pandemic, hospitals experienced an increase in the number of patients due to the rapid spread of the virus. The need for services in hospitals has increased compared to normal days. Therefore Healthcare Robot is needed that can help the service of patients and medical personnel in the hospital. The robot must be able to detect and recognize existing objects and put them in the expected place. The sensor itself here uses a camera depth or stereo camera. The input results are in the form of RGB-D Image, which we then convert to point cloud to get 3D information. Then the 3D information will be segmented and clustered to get the object to be detected using a RANSAC and Euclidean Cluster. Then feature extraction uses the Viewpoint Features Histogram (VFH) descriptor to get the characteristics of the object. Then the matching with the dataset using the Artificial Neural Network continued with Labelling and visualization of the result. With this system, the robot can detect and recognize objects around the hospital so that the robot can take action on these objects. At the end of this project, nine datasets and three scenes resulting from capture by the writer were tested. The results show an average accuracy of 90.77% for testing three multi-object scenes and 98.73% for testing one object.

Autonomous navigation is one of the important functions of the Healthcare Robot to produce obstacle-free movements in the social environment inhabited by humans. In carrying out its duties, the robot will do a lot of navigation from the origin to the destination. Healthcare Robot uses a Laser Range Finder to detect objects around the robot. The results of detection are distance and angle data from the object. Then, the data is used as input for the Fuzzy Inference System (FIS) process to produce an appropriate gain value to control static and dynamic force of the Social Force Model (SFM). The parameters of the SFM influence the robot's response to the detected object. To obtain the optimal gain value, the FIS is used to change the parameters adaptively. Adaptive parameters are used to prevent the robot from making unexpected navigational behavior that may be dangerous, threatening to others, and potentially self-destructive. From the tests carried out in two conditions, the robot successfully navigated from its initial position to the goal and was able to respond to objects around it with the percentage of success in all scenes was 79.9%.

Detecting dynamic and static objects is one of the important abilities for a mobile robot, including a healthcare mobile robot. As long as the robot carries out its duties, it will often encounter objects, both dynamic and static. Therefore, recognizing the dynamic and static objects is crucial. In this paper, the existence of human as an example of dynamic objects is obtained using a Laser Range Finder (LRF), since it can work faster than ordinary cameras. To recognize human data obtained from LRF, a sliding window process is applied to get the signal data of human feet which will then be classified using the Support Vector Machine (SVM). Meanwhile, to overcome the difference in the size of the human foot signal caused by the distance changes, a pyramid scanning process is also applied. Based on the experimental results obtained, human can be detected from a range of distances 1 up to 4.25 meter within 73 msec.