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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>Autonomous Sailboat Project</title>
<link rel="stylesheet" href="styles.css">
</head>
<body>
<header>
<h1>Optimal Control of an Autonomous Sailboat</h1>
</header>
<nav>
<a href="index.html">Home</a>
<a href="mailto:judsonsamaksh@gmail.com">Contact</a>
<a href="https://github.com/Samaksh0278">Github</a>
<a href="hobbies.html">Hobbies</a>
</nav>
<section id="SailboatProject">
<h2>Optimal Control of an Autonomous Sailboat</h2>
<p>
I developed a <strong>multi-level control system</strong> for an autonomous sailboat. The control system integrated multiple advanced optimization techniques to improve the sailboat's navigation and control in real-world conditions.
</p>
<!-- Labeled Image Section -->
<div class="image-container">
<h4 class="image-label">Robot Navigation Simulation</h4>
<img src="Pictures/optimal_sailboat_poster.jpg" alt="OCRL Poster">
</div>
<ul>
<li><strong>Route Optimization (RRT*):</strong> I implemented a global route optimization algorithm, known as Rapidly-exploring Random Trees (RRT*), which helps the sailboat find the most efficient path to its destination while avoiding obstacles in the environment.</li>
<li><strong>Trajectory Optimization and Model Predictive Control (MPC):</strong> After determining the global route, I used trajectory optimization and MPC to refine the sailboat's movements, reducing its <strong>navigation error by 15%</strong>. MPC continuously updated the sailboat's path based on current data, ensuring smooth, real-time adjustments to the environment.</li>
<li><strong>Improving Accuracy:</strong> By using data from the <strong>National Oceanic and Atmospheric Administration (NOAA)</strong>, I tested the system in a real-world scenario and improved the sailboat's accuracy by <strong>20%</strong>.</li>
<li><strong>Comparison of Techniques:</strong> I also compared two optimization methods, <strong>direct collocation</strong> and <strong>iLQR (Iterative Linear Quadratic Regulator)</strong>, to ensure that the system achieved both accuracy and efficient computation time.</li>
</ul>
<div class="gif-container">
<div class="gif-item">
<h4 class="gif-label">Los Angeles to San Francisco</h4>
<img src="Videos/lasf.historical.2024040606.gif" alt="GIF 1">
</div>
<div class="gif-item">
<h4 class="gif-label">San Francisco to Los Angeles</h4>
<img src="Videos/sfla.historical.2024040606.gif" alt="GIF 2">
</div>
</div>
<p>
This is what the entire model looks like upon deployment.
</p>
</section>
<footer>
<p>© 2024 Samaksh Judson. All Rights Reserved.</p>
</footer>
</body>
</html>