Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.

Artificial potential field (APF) is the effective realtime guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve oneobstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON) and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems.

The research proposes controlling DC motor angular speed using the Proportional Integral Derivative (PID) controller and hardware implementation using a microcontroller. The microcontroller device is Arduino Uno as data processing, the encoder sensor is to calculate the angular speed, and the motor driver is L298. Based on the hardware implementation, the proportional controller affects the rise time, overshoot, and steady-state error. The integral controller affects overshoot and undershoot. The derivative controller affects overshoot insignificantly. The best parameter PID is Kp=1, Ki=0.3, and Kd=0.1 with system response characteristic without overshoot and undershoot. Using various set point values, the controller can make the DC motor reach the reference signal. Thus, the PID controller can control, handle, and stabilize the DC motor system.

Line following robot is one of the popular robots commonly used for educational purposes. The most widely used sensors for the robots are photoelectric sensors. However, it is irrelevant, along with the development of autonomous vehicles and robotic vision. Robotic vision is a robot that can obtain information through image processing by the camera. The camera installed on the line following robot aims to detect image-based lines and to navigate the robot to follow the path. This paper proposed a method of image preprocessing along with its robot action for line-following robots. This includes image preprocessing such as dilation, erosion, Gaussian filtering, contour search, and centerline definition to detect path lines and to determine the proper robot action. The implementation of the robot is simulated using Webots simulator. OpenCV and Python are utilized to design line detection systems and robot movements. The simulation result shows that the method is implemented properly, and the robot can follow a different type of path lines such as zigzag, dotted, and curved line. The resolution of the cropped-image frame is the fundamental parameter in detecting path lines.

No abstract