Please use this identifier to cite or link to this item:
http://repository.ipb.ac.id/handle/123456789/114458
Title: | Pengembangan Algoritme Battery-Recharge Scheduling untuk Swarm-UAV pada Skenario Persistent Monitoring dalam Indoor Farming |
Authors: | Hardhienata, Medria Kusuma Dewi Priandana, Karlisa Lubis, Rinaldy Ansyah Pramana |
Issue Date: | 2022 |
Publisher: | IPB University |
Abstract: | Sektor pertanian di Indonesia saat ini tengah mengalami perkembangan menuju smart farming 4.0. Salah satu konsep smart farming adalah indoor farming. Persistent monitoring pada indoor farming sangat penting dilakukan untuk menjaga lingkungan yang aman sebagai tempat pertumbuhan tanaman. Pada umumnya, berbagai sensor statis digunakan untuk menjamin persistent monitoring pada indoor farming. Namun, sensor statis dapat mengalami kerusakan dan perbaikannya memerlukan waktu. Hal ini akan mengganggu proses persistent monitoring. Pada penelitian ini, swarm-UAV Crazyflie digunakan untuk menggantikan sensor-sensor statis yang rusak, sementara sensor-sensor tersebut diperbaiki. Penelitian ini bertujuan untuk mengembangkan algoritme Ant Colony Optimization (ACO) dan Cloud-Based Drone Navigation (CBDN) dalam melakukan penjadwalan pengisian daya pada Crazyflie agar dapat menggantikan sensor rusak untuk sementara waktu. Hasilnya, algoritme hasil modifikasi yang diusulkan dapat mengalokasikan Crazyflie ke wilayah task tertentu dan melakukan battery-recharge scheduling dengan meminimumkan total travel time. The agricultural sector in Indonesia is currently going towards smart farming 4.0. One of the smart farming concepts is indoor farming. Persistent monitoring in indoor farming is very important to maintain a safe environment as a place for plants to grow. In general, various static sensors are used to guarantee persistent monitoring in indoor farming. However, the static sensors can be broken and to repair it will take time. This will interfere with the persistent monitoring process. In this study, Crazyflie swarm-UAV is used to replace the broken static sensors, while the sensors were repaired. This study aims to develop Ant Colony Optimization (ACO) and Cloud-Based Drone Navigation (CBDN) algorithms to perform charging scheduling task on Crazyflie in order to temporarily replace the broken sensors. As a result, the proposed modified algorithm can allocate the Crazyflie to certain task areas and perform battery-recharge scheduling by minimizing the total travel time. |
URI: | http://repository.ipb.ac.id/handle/123456789/114458 |
Appears in Collections: | UT - Computer Science |
Files in This Item:
File | Description | Size | Format | |
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COVER.pdf Restricted Access | Cover | 2.39 MB | Adobe PDF | View/Open |
G64180027_Rinaldy Ansyah Pramana Lubis.pdf Restricted Access | Fullteks | 8.44 MB | Adobe PDF | View/Open |
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