Conflict-free dynamic route multi-AGV using dijkstra floyd-warshall hybrid algorithm with time windows

Autonomous Guided Vehicle is a mobile robot that can move autonomously on a route or lane in an indoor or outdoor environment while performing a series of tasks. Determination of the shortest route on an autonomous guided vehicle is one of the optimization problems in handling conflict-free routes that have an influence on the distribution of goods in the manufacturing industry's warehouse. Pickup and delivery processes in the distribution on AGV goods such as scheduling, shipping, and determining the route of vehicle with short mileage characteristics, is very possible to do simulations with three AGV units. There is a windows time limit on workstations that limits shipping. The problem of determining the route in this study is considered necessary as a multi-vehicle route problem with a time window. This study aims to describe the combination of algorithms written based on dynamic programming to overcome the problem of conflict-free AGV routes using time windows. The combined approach of the Dijkstra and Floyd-Warshall algorithm results in the optimization of the closest distance in overcoming conflict-free routes

Street Mark Detection Using Raspberry PI for Self-Driving System

Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for “hue” threshold of 0-179, “saturation” threshold of 0-30, and “value” threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method

INVESTIGATION OF SHORT CHANNEL EFFECT ON VERTICAL STRUCTURES IN NANOSCALE MOSFET

The recent development of MOSFET demands innovative approach to maintain the scaling into nanoscale dimension. This paper focuses on the physical nature of vertical MOSFET in nanoscale regime. Vertical structure is one of the promising devices in further scaling, with relaxed-lithography feature in the manufacture. The comparison of vertical and lateral MOSFET performance for nanoscale channel length (Lch) is demonstrated with the help of numerical tools. The evaluation of short channel effect (SCE) parameters, i.e. threshold voltage roll-off, subthreshold swing (SS), drain induced barrier lowering (DIBL) and leakage current shows the considerable advantages as well as its thread-off in implementing the structure, in particular for nanoscale regime

Street Mark Detection Using Raspberry PI for Self-driving System

Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for “hue” threshold of 0-179, “saturation” threshold of 0-30, and “value” threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method

Design of Automatic Switching Bio-Impedance Analysis (BIA) for Body Fat Measurement

Bioelectrical impedance analysis (BIA) is one method of measuring body fat levels by distinguishing the fat mass and non-fat mass based on body composition assessment. This research designs a system to measure the body fat percentage by BIA method. The system is capable of measuring BIA with automatic switching between four different electrode schemes, i.e cross-sectional, hand-to-hand, hand-to-foot, and foot-to-foot. Two electrodes are to conduct current into the body, while other two electrodes are utilized to measure the voltage from the body. The alternating current is injected with frequency of 50 kHz. Automatic switch in the form of multiplexers and demultiplexers controls the sequence of BIA measurement methods. Microcontroller process the data and the result is displayed on LCD. A keypad is used to input related body parameters, i.e height, weight, age, and gender. The measurement tests shows that the BIA works as intended, while the comparison with commercial BIA reveals maximum relative error of 4.6 % and the highest standard deviation of 2.2%

Study on the Analytical Model of non-planar MOSFET

In the recent development of MOSFET, non-planar structure has been identified as promising structure for next device generation. The advanced scaling of device implies that more sophisticated model is required due to the limitation of the existing models for application in nano scale. Analytical model for non-planar MOSFET model is discussed in this paper, especially for device with pillar. The concern of channel shape and structure is elaborated as well. The result shows the shift in subthreshold characteristic due to the presence of recessed region in the channel with the simulated model

Hasil Turnitim: "Particle swarm optimization (PSO)-based self tuning proportional, integral, derivative (PID) for bearing navigation control system on quadcopter"

Unmanned Aerial Vehicle (UAV) is an unmanned aircraft that is controlled manually or automatically over long distances. The quadcopter UAV is rapidly developed in recent years due to various purposes. One of the quadcopter navigation system, the UAV moves towards different coordinates by controlling vertical axis rotation angle (yaw) or bearing. In this research, we propose self-tuning proportional integral derivative (PID) control using particle swarm optimization (PSO) method for bearing navigation. Global positioning system (GPS) is used to determine the coordinates of bearing angle to the destination. HMC5883L compass sensor is used to calculate the actual angle of the quadcopter from the earth electromagnetic field. Based on the test results, the quadcopter successfully holds fixed coordinates with settling time at 6.4s and average error after settling time is 5.4o. Based on test result of coordinate changes, the quadcopter is able to reach the aim as fixed coordinates with average error of 7.9o. In the experiment with disturbance, an average offset error of 1.89o and settling time of 4.1 seconds has been achieved. The best PSO self-tuning limits are obtained at Kp = 0.15 to 0.3, Ki = 0.06 to 0.6, and Kd = 0.005 to 0.1. The PSO values used were C1 = 1.5, C2 = 2 and the weight of inertia from 0.7 to 1.2

Rotated Rectangular Slots And Mirrored Inversed Cantor-sets On Ultrawideband Antipodal Vivaldi Antenna

Variants of antipodal Vivaldi antenna (AVA) design suitable for access point working on 0.5 – 6.0 GHz are proposed in this paper. The novel designs were produced by employing three novel techniques to conventional AVA: (i) rotated-slot pattern to shift down the frequency cutoff and enhancing bandwidth, (ii) curve design to miniaturize rotated-slot-inserted antipodal Vivaldi, and (iii) fractal-director (Cantor set) to increase the gain of antipodal Vivaldi. Using FR4 (relative permittivity of 4.4) with an overall dimension of 300 mm x 143 mm x 1.6 mm the antenna designs are able to work at a frequency of 0.472 GHz to higher than 6 GHz with a maximum gain of 11.9 dBi