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project_page_2.html
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<!DOCTYPE html>
<html class="no-js" lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>My Portfolio</title>
<link rel="shortcut icon" href="LogoFinal.ico" type="image/x-icon">
<link rel="icon" href="LogoFinal.ico" type="image/x-icon">
<link href="https://fonts.googleapis.com/css?family=Lato:300,400,700,900" rel="stylesheet">
<link rel="stylesheet" href="libs/font-awesome/css/font-awesome.min.css">
<link href="css/bootstrap.min.css" rel="stylesheet">
<link href="css/styles.css" rel="stylesheet">
</head>
<body>
<div id="mobile-menu-open" class="shadow-large">
<i class="fa fa-bars" aria-hidden="true"></i>
</div>
<!-- End #mobile-menu-toggle -->
<header>
<!-- <div id="mobile-menu-close">
<span>Close</span> <i class="fa fa-times" aria-hidden="true"></i>
</div>
<ul id="menu" class="shadow">
<li>
<a href="index.html">UI Design</a>
</li>
<li>
<a href="#education">Education</a>
</li>
<li>
<a href="#projects">Projects</a>
</li>
<li>
<a href="#skills">Skills</a>
</li>
<li>
<a href="#contact">Contact</a>
</li>
</ul> -->
</header>
<!-- End header -->
<div id="lead" class = "CSAI">
<div id="lead-content">
<h1>Swarm Pathfinding Algorithms<br> </h1>
<a href="index.html" class="btn-rounded-white">Return Home</a>
</div>
<!-- End #lead-content -->
<!-- <div id="lead-overlay"></div> -->
<div id="lead-down">
<span>
<i class="fa fa-chevron-down" aria-hidden="true"></i>
</span>
</div>
<!-- End #lead-down -->
</div>
<!-- End #lead -->
<div id="about">
<div class="container">
<div class="row">
<div class="col-md-2">
<h2 class="heading">Project Summary</h2>
</div>
<div>
</div>
<div class="col-md-4">
<p>
This project focuses on swarm robotics and its application to multi-agent path planning in unfamiliar environments with unknown static obstacles.
Three emerging algorithms are evaluated: Multi-Agent Deep Deterministic Policy Gradient (MADPG).
Hybrid Simplified Grey Wolf Optimization with Modified Symbiotic Organism Search (HSGWO-MSOS), and Improved Artificial Potential Field (APF).
The A* Search algorithm is used as a baseline algorithm for comparison with experimental methods.
We compared path planning of these algorithms in a 2D simulated environment, considering execution time, completeness, scalability, and efficiency.
In order to test and evaluate these algorithms, we created a swarm simulation software using python's pygame package.
The simulation software simulates a number of agents navigating an environment to a known goal, through unknown obstacles.
</p>
<br>
<p>I worked on a team of 4, and was the leading software contributor.</p>
</div>
<div class="col-md-4">
<img source src="images/image28.gif" width = "500"/>
<img src="images/image25.gif" width = "500">
</div>
</div>
<div class="row">
<div class="col-md-2">
<h2 class="heading">Data Analysis</h2>
</div>
<div>
</div>
<div class="col-md-4">
<p>
I used batch scripting in order to generate data from hundreds of simulations for each algorithm.
Data such as runtime, pathlength, swarm size, completion percentage, and obstacle difficulty were collected.
We then exported data to excel and used it to compare the 3 algorithms in our report.
</p>
</div>
<div class="col-md-4">
<img src="images/Comparison of Average Path Length in Environment 3_(High Obstacle Complexity) (1).png" width="500">
</div>
<!-- <div class="col-md-4">
<video controls width="300">
<source src="images\Estop.mp4" type="video/mp4" />
</video>
</div> -->
</div>
</div>
</div>
<!-- End #about -->
<div id="contact">
<h2>Get in Touch</h2>
<h3>Email: harrisw522@gmail.com</h3>
<h3>Phone: 781-690-3302</h3>
<h3>Location: Belmont, MA</h3>
<!-- End #contact-form -->
</div>
<!-- End #contact -->
<footer>
<div class="container">
<div class="row">
<div class="col-sm-5 copyright">
<p>
Copyright © <span id="current-year">2023</span> Wyatt Harris
</p>
</div>
<div class="col-sm-2 top">
<span id="to-top">
<i class="fa fa-chevron-up" aria-hidden="true"></i>
</span>
</div>
<div class="col-sm-5 social">
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