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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>Software - Tips & Tricks</title>
<meta content="" name="descriptison">
<meta content="" name="keywords">
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<link href="assets/img/favicon.png" rel="icon">
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<body>
<!-- ======= Header ======= -->
<header id="header" class="header-transparent">
<div class="container">
<div id="logo" class="pull-left">
<!-- <a href="index.html"><img src="assets/img/logo.png" alt=""></a> -->
<!-- Uncomment below if you prefer to use a text logo -->
<h1><a href="#hero">IEEE VSS</a></h1>
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<li class="menu-active"><a href="index.html">Home</a></li>
<li><a href="index.html#about">About Us</a></li>
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<h2>Tips - Software</h2>
<ol>
<li><a href="index.html">Home</a></li>
<li><a href="#">Tips&Tricks</a></li>
<li>Software </li>
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<!-- ======= About Section ======= -->
<section id="softia">
<div class="container" data-aos="fade-up">
<center><h2>Artificial Intelligence Tips</h2></center>
<br>
<div class="row softia-container">
<div class="col-lg-6 content order-lg-1 order-2">
<h3>Path Planning</h3>
<p> Path Planning is computational problem to find a sequence of valid configurations that moves the object from the source
to destination. One example is the Rapidly-exploring Random Trees (RRT)</p>
<br>
<h4>RRT</h4>
<p>
In essence, an RRT planner searches for a path froman initial state to a goal state by expanding a searchtree.
First, an extend function calculates a new state that can be reached from the target state by some incremental distance. If a collision with an obstacle in the
environment would occur by moving to that new state, then a default value of type state is
returned to capture the fact that there is no “successor” statedue to the obstacle. In general, any heuristic methods suitable for control of the robot can
be used here. The heuristic just neet to detect when a state would hit the robot, however, the better the heuristic,the fewer nodes the planner will need to
expand on average. Next, the algorithm provide an estimate of the time or distance that estimates how long
repeated application would take to reach the goal. Finally, returns a state drawn uniformly from the state space of the environment.
</p>
<br>
</div>
<div class="col-lg-6 background order-lg-2 order-1" data-aos="fade-left" data-aos-delay="100"></div>
</div>
<h3>Decision Making</h3>
<p>
Decision Making (DM) is one of basic blocks of soccer robot navigation system. DM analyses the current state of the world model and makes decisions about
new positions of robots, for example.
<h4>Behavior tree</h4>
BT is goal oriented. Each tree is assigned a goal, that will be achieved. The robot behavior is a control law that satisfies a set of constraints to
achieve a particular goal. Each behavior is defined by the set of actions.
BTs perform a number of artificial intelligence (AI) techniques such as Finite State Machines, Scheduling, Planning, and Action Execution.
A BT enables modularity, making states nested within each other and thus forming a tree-like structure, and restricting transitions to only these nested states.
The root node branches down to the tree until the leaf nodes are achieved.
BT replace th egrowing mess of state transitions of finite state machines (FSMs) with a more restrictive but also more structured traversal defining approach.
Because of this, BTs easily define complex states.
</p>
<br>
<br>
</div>
<br>
<br>
</section>
<section id="soft">
<div class="container" data-aos="fade-up">
<center><h2>Computer Vision</h2> </center>
<br>
<div class="row soft-container">
<div class="col-lg-6 content order-lg-2 order-2">
<h3>Multi-Object Filter</h3>
The mainly purpose of a Multi-Object is avoid multiple objects with the same ID being tracked by the vision system. This is a commonly problem when the system uses more than one camera or the color segmentation creates more than one object to the same color pattern. So, the real application of this filter is unify objects with same ID.
<br>
<br>
<h3>Noise Filter</h3>
The Noise Filter aims to avoid "ghost objects" which can appear due problems in color segmentation. This filter presents a resistence to new objects, maintaining the map with same quantity of objects.
To implement this filter, it is possible to use a timer, verifying if the object cotinue existing after a time window. Other aproach uses a counter and every frame where the object appears, one point is added to a confidency counter.
<br>
<br>
<h3>Loss Filter</h3>
The Loss Filter aims to avoid deactivate objects which desappear during small amount of thime due problems in color segmentation caused by shadows or light modification. This filter presents a persistence to objects that already exists in the system. With him, even if an object disappears, it is possible to continue tracking its position using the Kalman Filter.
</div>
<div class="col-lg-6 background order-lg-1 order-1" data-aos="fade-right" data-aos-delay="100"></div>
</div>
<br>
<br>
<br>
<div>
WARNING: The algorithms and solutions described are considerable simple and works as examples for first studies.
It is recomended an deeper research in other solution to create a competitive team.
</div>
</div>
<br>
<br>
<center>More information in <a href="#">VSS AI</a> and <a href="https://github.com/IEEEVSS/VssWiki/wiki/Filters-to-Vision-System">VSS Vision</a></center>
<br>
<br>
</section>
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