DEVELOPMENT AND IMPLEMENTATION OF EMBEDDED SYSTEM ON GANESHBLUE UNDERWATER GLIDER

Autonomous Underwater Glider (AUG) requires integration of several systems in order perform maritime exploration mission. Deficiensies of a system can also affect performance of other systems. Performance of sensor data acquisition process will affect AUG ability to estimate its position, reachin...

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Bibliographic Details
Main Author: Prabudi Wicaksa, Dhimas
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/53196
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Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:Autonomous Underwater Glider (AUG) requires integration of several systems in order perform maritime exploration mission. Deficiensies of a system can also affect performance of other systems. Performance of sensor data acquisition process will affect AUG ability to estimate its position, reaching destionation point, and cause unsuitable AUG movements. Likewise, delays in the Finite State Machine (FSM) will also cause delays in AUG state, event, or action. This research aims to optimize embedded system in AUG GaneshBlue and develop algorithms to make it able to to perform maritime exploration missions optimally. Optimization is done by code refactoring on implemented navigation, guidance, and control system. Optimization evaluation is based on software metrics in Halstead metrics, cyclomatic complexity, and maintainability index of the source code. Algorithm development for multi-coordinates maritime exploration mission is carried out by integrating AUG, Ground Control Station (GCS), and a website. AUG also has capability to perform failsafe action while on a mission. Based on test results, optimization resulted in a reduction of average error for NGC system interval on primary processors with mutlthread architecture reaching 89.73% and a reduction of average error for data acquisition interval on secondary processors with Round Robin with Interrupt architecture reaching 99.69%. Based on software metrics, maintainability index is increased by up to 12%, reduction on Lines of Code (LOC) by 36%, and an increase in the quality of implemented code based on Halstead metrics.