Robot Keseimbangan Beroda Dua dengan Sistem Kontrol Keseimbangan dan Posisi Menggunakan Metode PID Bertingkat

The two-wheeled balancing robot is a robot that will maintain its balance to stay upright by using two wheels. This robot cannot be stable when the condition is upright and requires a control mechanism when moving. There are at least two control mechanisms in this robot, first is balance control, and the second is position control. The cascade PID method is proposed as a control mechanism, which consists of balance control as primary control and position control (distance and direction) as a secondary control. This method has been applied to robots. Based on the first, second, and third experiment, the best configuration of cascade PID control is PID for the balance control block, PD for distance control, and PD for direction control. By using cascade PID control, the two-wheeled balancing robot has been able to balance itself with oscillations ranging from ± 15.00 degrees when moving and it can move towards the ordered position with the error position from the target. Fourth experiment position error is (0.17, -0.26) and (0.45, -0.43) for the fifth experiment

An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using K-Means Clustering

Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%

Surface 3D Scanner Using TIME of Flight Ranging Sensor with Cylindrical Coordinate System

3D scanner that uses image sensors requires the role of a computer that includes a data generator, data acquisition, and visual display. In a simply system, it can be designed the sensory system uses non-imagery sensor so the role of the data generator can be handled by the microcontroller. This research aims to make a simple 3D scanner using inexpensive non-imagery Time of Flight VL53L0X sensor and data processing can be processed directly by the microcontroller. The results of sensor distance measurements are processed on the microcontroller and desktop application. The distance and angle values are converted into Cartesian coordinate using cylindrical coordinate system. The scan results of the cubes, prisms and cylinder are similar with the reference object, but the results of the pyramid test at the top cannot be scanned properly due to the narrow surface. The laser beam from the emitter cannot bounce back to the collector properly makes distance reading is inaccurate and causes error in the point cloud conversion. The comparison error between the side of the scan results and the reference object is between 2.54-39.8%. The surface of objects with bright color has a smaller error than those with dark color. The comparison error of the height of the scan results with the reference object is between 5-32%. The angle of the emitter exclusion cone and the collector exclusion cone sensor affects the error at the side and height of the scan results