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<!DOCTYPE html>
<!--[if IE 9]> <html lang="en" class="ie9"> <![endif]-->
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<html lang="en">
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<head>
<meta charset="utf-8">
<title>AGV IIT Kharagpur</title>
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<li class="active"><a href="#banner">Home</a></li>
<li><a href="#services">Vision</a></li>
<li><a href="#about">Research</a></li>
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<br><br> <h1>Autonomous Ground Vehicle</h1>
<h3 class="padding-top30">IIT Kharagpur</h3>
</div>
</div>
</div>
</div>
</div>
<div class="container">
<div class="row " style="margin-top: 0px;">
<div class="col-sm-12">
<h2><strong>Autonomous Ground Vehicle</strong></h2>
<p> Team AGV is a multi-disciplinary research group aimed at building a fully operational self-driving car.
Team AGV presented its latest "Eklavya" series robot, "Eklavya 4.0", in IGVC-2015 organized at Oakland University,
Detroit, Michigan, USA from June 5th - June 8th, and came out with a good performance which includes qualification
on most of the qualifying parameters in the Auto navigation challenge and a significantly improved
performance in the design competition where it stood 20th among the 42 teams from all over the world,
and the best among all other participants from India and being only Indian team to go on this far.
</p>
</div>
</div>
</div>
</section>
<section class="section transprant-bg pclear secPadding">
<div class="container no-view" data-animation-effect="fadeIn">
<h2 id="services" class="title text-center">Our Vision</h2>
<div class="space"></div>
<h4>
"Our vision is to have a world without road accidents ; a place where people can commute
without fear, to build the world’s most advanced and cost effective self-driving car."
</h4>
<div class="vision_top_text">
The present transportation system is getting increasingly expensive and inefficient.
<br>
<ul>
<li>Over $230 billion are spent on car crashes every year, majority of which are due to human errors.</li>
<li>Congestion in total amounts to 10.5 billion litres of wasted petrol each year.</li>
<li>Over 4.2 billion hours are wasted in traffic every year.</li>
<li>There has been an increase in number of accidents due to human errors such as drinking and driving.</li>
</ul>
The solution to all these problems is a selfdriving car. With this inspiration, we have started Autonomous Ground
Vehicle (AGV) Research Group in IIT Kharagpur. We aspire to make India’s first fully operational self-driving car.
</div>
<div class="row">
<h2>Goals : </h2>
<div class="col-md-6 col-sm-6">
<div class="media block-list">
<div class="col-md-4 col-sm-4">
<a href="http://www.sparktherise.com/program-detail/driverless-car-challenge" target="_blank">
<img width="120px" alt=" " src="images/mahindra.png">
</a>
</div>
<div class="media-body col-md-8 col-sm-8">
<h3 class="media-heading"><b>Mahindra Rise Prize</b></h3>
<div class="vision_left_text">
<blockquote>
<p>India has been on the verge of greatness for too long. It's time to change that.
It’s time to make the world sit up and take notice. To create disruptive solutions
that transform lives. To lead in enterprise and innovation. To see "Made in India"
mean best in the world. This is Mahindra Rise Prize challenge aims at.
We at AGV have been selected in the first round of this challenge and we are
looking foreward to applying our technology to a mahindra Vehicle makinb
it fully Autonomous.
</p>
</blockquote>
</div>
</div>
</div>
</div>
<div class="col-md-6 col-sm-6">
<div class="media block-list">
<div class="col-md-4 col-sm-4">
<a href="http://www.igvc.org" target="_blank">
<img width="130px" alt=" " src="images/igvc.png">
</a>
</div>
<div class="media-body col-md-8 col-sm-8">
<h3 class="media-heading"><b>Intelligent Ground Vehicle Challenge</b></h3>
<div class="vision_right_text">
<blockquote>
<p>The IGVC offers a design experience that is at the very cutting edge of engineering education.
It is multidisciplinary, theory-based, hands-on, team implemented, outcome assessed, and based
on product realization. It encompasses the very latest technologies impacting industrial
development and taps subjects of high interest to students. Design and construction of an
Intelligent Vehicle fits well in a two semester senior year design capstone course, or an
extracurricular activity earning design credit. The deadline of an end-of-term competition is a
real-world constraint that includes the excitement of potential winning recognition and financial
gain.
Students solicit and interact with industrial sponsors who provide component hardware and advice, and
in that way get an inside view of industrial design and opportunities for employment.
Team AGV has been constantly participating in Intelligent Ground Vehicle Compitition and making a mark.
</p>
</blockquote>
</div>
</div>
</div>
</div>
</div>
<br><hr />
<h3><b>Research Applications : </b></h3>
<br><br>
<div class="row">
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-gear"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>Mine Mapping</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>
The lack of accurate maps of underground mines frequently causes mine accidents.
Hazardous operating conditions and difficulty in access makes Robotic Exploration
and Mapping an immediate and safe choice.The mapping algorithms used in the
self driving car can also be applied for mines.
</p>
</blockquote>
</div>
</div>
</div>
</div>
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-gears"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>Construction Mapping</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>Large scale construction projects might involve exploration of areas inaccessible
or life threatening to workers. For such scenarios, a portable mapping solution could
be hosted on a small flying quadrotor.</p>
</blockquote>
</div>
</div>
</div>
</div>
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-bank"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>City Mapping</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>The traditional surveying techniques for generating maps for city planning requires
thousands of man-hours and are very expensive. Large cities can be mapped accurately and
efficiently using a self driving car equipped with 3D laser mapping technologies.
This can cut down on a lot of manual work and save time. With this view, we undertook
the DIGITAL KGP PROJECT in which we mapped some parts of the IIT Kharagpur campus using
the 3D ENVIRONMENT MAPPING Technology.
</p>
</blockquote>
</div>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-moon-o"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>Night Time Driver Assist</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>The use of special sensors in autonomous driving systems can detect approaching vehicles
and road boundaries under poor lighting conditions. This feature could be widely used in
vehicles as an essential safety measure.
</p>
</blockquote>
</div>
</div>
</div>
</div>
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-road"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>Lane Departure Warning System</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>
This system warns the driver as soon as the vehicle starts moving out of the lane.
If the driver,for some reason, avoids the warning, the system takes over and moves
the vehicle back into the lane. This technology completely removes the human error
involved in the process.
</p>
</blockquote>
</div>
</div>
</div>
</div>
<div class="col-md-4 col-sm-4">
<div class="media block-list">
<div class="media-left">
<i class="fa fa-ambulance"></i>
</div>
<div class="media-body">
<h3 class="media-heading"><b>Collision Detection System</b></h3>
<div class="vision_botton_text">
<blockquote>
<p>
A sensing system has a wider field of view compared to an average human being.
It is also capable of running calculations at over 2.5 GHz. Owing to these qualities,
a sensing system can warn the driver beforehand about a probable collision.
</p>
</blockquote>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- section end -->
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<div class="container">
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<div class="col-md-12">
<h1 id="about" class="title text-center">Research Areas</h1>
<!-- Items start -->
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<div class="col-sm-3 col-md-3 isotope-item Tech" id="Research_tech">
<div class="image-box">
<div class="overlay-container">
<img src="images/Research/Comp_Vision/comp_vision.jpg" alt="">
<a class="overlay" data-toggle="modal" data-target="#project-1">
</a>
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<a class="btn btn-default btn-block" data-toggle="modal" data-target="#project-1">Computer Vision</a>
</div>
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<h2 class="modal-title" id="project-1-label">Computer Vision</h2>
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<div class="col-md-12" id="comp_vision_popup">
<h3> Introduction </h3>
<p> A computer vision system processes images acquired from an electronic camera, which is like the human vision
system where the brain processes images derived from the eyes. Computer vision is a rich and rewarding topic
for study and research for electronic engineers, computer scientists and many others. There are now many vision
systems in routine industrial use: cameras inspect mechanical parts to check size, food is inspected for quality,
and images used in astronomy benefit from computer vision techniques. Forensic studies and
biometrics (ways to recognize people) using computer vision include automatic face recognition
and recognizing people by the 'texture' of their irises. </p>
<br>
<br>
<h3>Our Progress so far :</h3>
<p>
We as team AGV dream to build an autonomous car. This is viewed as replacing the manual driver with an autonomous system
capable of navigating the road. An integral part in the realisation of this dream is perception.
Computer vision, being the backbone of perception, has been a major focus of our team.
Our major objectives have been :-
<br>
1. Removal of visible obstacles and potential dangers to the obstacle<br>
2. Extraction of lane boundaries from the input image to ensure the vehicle positioning wrt road<br>
3. Removal of remaining features that are not required for the safe locomotion of the vehicle<br>
4. Creation of occupancy grid map of the surrounding.<br>
Given the environment we are working on is dynamic, we need to experiment with various techniques for feature extraction and noise removal. Most of our work has been in developing intensity invariant algorithms and working on region independent algorithms for feature extraction. Our workflow so far has been the following :-
<br/><br/>
</p>
<img src="images/Research/Comp_Vision/flow_chart.png">
<br/><br/>
As we tried on different algorithms and channels, we found out that the HSV colour system
is most favoured to remove obstacles for IGVC setup. Also, the lane segments are best retrieved
for BGR color system. The conversion of image view from perspective to top requires a matrix transform
of the image by multiplying it with a transformation matrix.
Also, the input image may contain information above the horizon on some occasions.The team
devised a unique sky-removal algorithm to deal with this issue and this increased processing
speeds by at least 25% . The algorithm works on column-wise standard mean deviation in order to
determine the boundary at each column and then determines the horizon line by averaging out the
boundary points at each column.
For lane segment extraction, blue and red channels were split, threshold value calculated and
finally merged. This gave least noise and best retention of lanes.
The lane classification required nearest-neighbour algorithm and it classifies each lane segment
as left or right part. Then , we tried some lane-fitting algorithms to fill in the gaps between
lane segments of same side. But since, curve regression was a very costly operation, we simply
did straight line approximation of the road in close proximity of present bot location.
<h4>Challenges faced:- </h4><br/>
1.<b> Obstacle removal-</b>
The algorithm is limited to obstacles of certain color, such as the
cones and barrels used to indicate diversion in roads, can only be removed
from the image. The algorithm is intensity invariant and thus does not depend
on minor light changes. But the limitation of color required a separate obstacle
detector , LIDAR. <br/>
2. <b>Grass Removal-</b>
Algorithms based on certain color characteristics and image features
do not work well universally due to presence of different type of dry grass in
different areas. So, machine learning was used on the intensity invariant form
of the image. This gave better results although the performance of the system
was compromised. <br/>
3.<b> Lane segmentation-</b>
Curve regression to complete broken lanes was very costly in
terms of time and space. So, we used linear approximation and setting a
dynamic target according to the immediate curvature of the lane. Also the image
was split into various parts vertically and the part-wise mean points of each parts
were used for regression.This gave better results at low cost. <br/>
4.<b> Transformation matrix-</b>
The transformation matrix is subject to angle of inclination and
position of the camera. Hence, every time the parameters are changed, it needs
to be calibrated properly.
<br/><br/><br/>
<h3>The Road Ahead</h3>
<p>
We plan to work on motion analysis in the recent future. Several tasks relate to motion estimation where an
image sequence is processed to produce an estimate of the velocity either at each points in the image or in
the 3D scene, or even of the camera that produces the images . Examples of such tasks are:
<br/>
<ul>
<li><b>Tracking</b> – following the movements of a (usually) smaller set of interest points or
objects (e.g., vehicles or humans) in the image sequence.
</li>
<li><b>Optical flow </b>– to determine, for each point in the image, how that point is moving relative to
the image plane, i.e., its apparent motion. This motion is a result both of how the corresponding 3D point
is moving in the scene and how the camera is moving relative to the scene.
</li>
</ul> We are planning to come up with proper system and simulation
software to test our algorithms and plans before testing it during real-time testing on the bot.
</p>
<br><br>
<h3>Result :-</h3><br/></br>
<div class="row">
<div class="col-md-3 col-sm-6">
<img style="width:250px" src="images/Research/Comp_Vision/c1.png">
<figcaption><b>ORIGINAL IMAGE</b></figcaption>
</div>
<div class="col-md-3 col-sm-6">
<img style="width:250px" src="images/Research/Comp_Vision/c2.png">
<figcaption><b>OBSTACLE IMAGE OUTPUT</b></figcaption>
</div>
<div class="col-sm-6"></div>
<div class="col-md-3 col-sm-6">
<img style="width:250px" src="images/Research/Comp_Vision/c3.png">
<figcaption><b>GRASS REMOVAL OUTPUT</b></figcaption>
</div>
<div class="col-md-3 col-sm-6">
<img style="width:250px" src="images/Research/Comp_Vision/c4.png">
<figcaption><b>CHANNEL SEPARATION AND EXTRACTION</b></figcaption>
</div>
</div>
<br>
<hr />
<h3>Related Videos :- </h3> <br><br>
<iframe width="560" height="315" src="https://www.youtube.com/embed/_BWnIQY_X98?list=PLCFD85BC79FE703DF" frameborder="0" allowfullscreen></iframe><br/><br/>
<iframe width="560" height="315" src="https://www.youtube.com/embed/eZKUAwPNsfY?list=PLCFD85BC79FE703DF" frameborder="0" allowfullscreen></iframe>
<br><br>
<hr />
<h3>Team Members :-</h3> <br><br>
<div class="row">
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/harsh.jpg">
<figcaption><B>HARSH GUPTA</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/atal.jpg">
<figcaption><B>ATAL ASHUTOSH AGRAWAL</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/vikram.jpg">
<figcaption><B>VIKRAM MOHANTY</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/arna.jpg">
<figcaption><B>ARNA GHOSH</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/krishna.jpg">
<figcaption><B>KRISHNA BAGADIA</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/aranya.jpg">
<figcaption><B>ARANYA DAN</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6"></div>
<div class="col-md-3 col-sm-6"></div>
</div>
</div>
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</div>
<a class="btn btn-default btn-block" data-toggle="modal" data-target="#project-2">Machine Learning</a>
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<div class="col-md-12">
<h3> Introduction </h3>
<p>
Machine Learning is the science of getting computers to act without being explicitly programmed. In the
past decade, machine learning has set a firm foot in the fields varying from practical speech recognition,
efficient web search, financial prediction, and a vastly improved understanding of the human genome, to
name a few. It is so pervasive today that one probably uses it dozens of times of a day, without knowing it.
Presently, it is considered to be the set of algorithms, which are closest to realise the ultimate AI dream.
</p>
<h3>In Perception</h3>
<p>
At AGV, the Machine Learning Group works closely with the Computer Vision Team to develop algorithms,
which enhance the robots awareness regarding its environment. The primary focus revolves around the
following fields of research :
<br>
<b>
<ul>
<li> Object Detection and Classification</li>
<li> Extracting drivable region in a given image</li>
<li>Object tracking</li>
<li>Event forecasting</li>
</ul>
</b>
</p>
<h3>Beyond Vision</h3>
<p>
Apart from the above mentioned, Machine Learning plays an important role in assisting other modules to
improve the robustness of their algorithms. In this context, it is relevant in the following fields :
</p>
<br>
<b>
<ul>
<li> Parameter optimization in Controls</li>
<li> Improving heuristics in Planner</li>
<li> Experience based models for Planner</li>
<li> Modelling of the environment for SLAM</li>
</ul>
</b>
<br>
Overall, Machine Learning along with the recent advances in Neural Networks, provides a broad scope of
research in the field specially in developing Artificial Intelligence and Autonomous Robotics.
<h3>Related Videos</h3> <br>
<iframe width="560" height="315" src="https://www.youtube.com/embed/ZiIYIQ0Ozgs" frameborder="0" allowfullscreen></iframe><br><br><br>
<iframe width="560" height="315" src="https://www.youtube.com/embed/DwM-BRp_v5A" frameborder="0" allowfullscreen></iframe><br><br><br>
<iframe width="560" height="315" src="https://www.youtube.com/embed/mZx_GlSownA" frameborder="0" allowfullscreen></iframe><br><br><br>
<br><br><hr />
<div class="row">
<div class="col-md-11 col-sm-12">
<img src="images/Research/Machine_Learning/pic1.png" alt="">
</div>
<div class="col-md-1"></div>
</div>
<br><br>
<div class="row">
<div class="col-md-11 col-sm-12">
<img src="images/Research/Machine_Learning/pic2.png" alt="">
</div>
<div class="col-md-1"></div>
</div>
<br><br>
<hr />
<br>
<h3> Team Members</h3>
<div class="row">
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/vikram.jpg">
<figcaption><B>VIKRAM MOHANTY</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/kawaljeet.jpg">
<figcaption><B>KAWALJEET KUMAR</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/abinash.jpg">
<figcaption><B>ABINASH MEHER</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/manuj.jpg">
<figcaption><B>MANUJ AGRAWAL</b></figcaption>
</figure>
</div>
</div>
<br><br><hr />
<h3>KEY FACTS</h3>
<ul class="keyfac">
<li>Extraction of accessible path from image</li><br/>
<li>Object extraction and tracking</li><br/>
<li>Controls optimisation</li><br/>
<li>Environment modelling for SLAM</li><br/>
<li>Advancement in planning with experience</li><br>
<li>Improvement in decision making</li><br>
</ul>
</div>
</div>
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</a>
</div>
<a class="btn btn-default btn-block" data-toggle="modal" data-target="#project-3">Localization And Slam</a>
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<div class="col-md-12">
<h3><b>INTRODUCTION</b></h3> <br><br>
<p>
Robot localization denotes the robot's ability to establish its own position and orientation within the frame
of reference. Localization involves one question: Where is the robot now? Or, robo-centrically, where am I,
keeping in mind that "here" is relative to some landmark (usually the point of origin or the destination)
and that you are never lost if you don't care where you are.
All localization techniques generally provide two basic pieces of information:
<br>
1.What is the current location of the robot in some environment?<br>
2.What is the robot’s current orientation in that same environment?<br><br>
In order to track the current position of the robot, It uses sensors which give data along with errors.cAs the
environment doesn’t behave ideally as we expect which further adds on to the error of the sensor data. So,
numerous filters are used to minimize the erroneous results and approximately predict the position of the robot.
The commonly used filters are:<br>
1.Bayes Filter<br>
2.Kalman Filter<br>
3.Extended Kalman Filter<br>
4.Particle Filter<br>
<br>
Kalman Filter is one of the most widely used filters in mobile robotics. It is a technique from estimation theory that
combines the information of different uncertain sources to obtain the values of variables of interest together with the
uncertainty in these.The filter has been successfully applied in many applications, like missions to Mars, and automated
missile guidance systems. <br>
<br><br>
<img src="images/Research/Localization/1.png">
<br/><br/>
Implementation of localization requires a system and measurement models that are needed by the filter which are:<br>
<ul>
<li>A model of the Robot Kinematics</li>
<li>A GPS-like sensor</li>
<li>A landmark-based sensor.</li>
</ul>
<br><br>
<h3>SLAM</h3> <br><br>
Simultaneous Localization and Mapping (SLAM) is a method of determining the location of a vehicle in an entirely
unknown environment and simultaneously mapping the environment. Its usage has grown exponentially over the recent
years. It has been used in unmanned aerial vehicles, autonomous underwater vehicles, planetary rovers, newly
emerging domestic robots and even inside the human body.
SLAM consists of multiple parts:<br>
<ul>
<li>Landmark extraction</li>
<li>Data association</li>
<li>State estimation</li>
<li>State update</li>
<li>Landmark update</li>
</ul>
SLAM is applicable for both 2D and 3D motion. Mapping deals with the questions "What does the world look like ?" and
localization deals with the question "Where am I ?".<br><br>
SLAM deals with the problem of building a model leading to a new map, or repetitively improving an existing map, while
at the same time localizing the robot within that map. SLAM will always use several different types of sensors to
acquire data with statistically independent errors. Statistical independence is the mandatory requirement to cope with
metric bias and with noise in measures.The optical sensors used in SLAM are 2D lidar or a 3D lidar, 2D Cameras, Sonar.
For the localization, it uses the information from odometry, IMU and GPS.<br/>
<br>
</p>
<div class="row">
<div class="col-md-6 col-sm-12">
<figure>
<img style="width:450px" alt="The Pulpit Rock" src="images/Research/Localization/2.gif">
</figure>
</div>
<div class="col-md-6 col-sm-12">
<figure>
<img style="width:450px" alt="The Pulpit Rock" src="images/Research/Localization/3.png">
</figure>
</div>
</div>
<h3>Related Videos</h3>
<br>
<iframe width="560" height="315" src="https://www.youtube.com/embed/G_vtm46eGtU" frameborder="0" allowfullscreen></iframe>
<br>
<br><hr />
<h3> Team Members</h3>
<div class="row">
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/atal.jpg">
<figcaption><B>ATAL ASHUTOSH AGRAWAL</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/vikram.jpg">
<figcaption><B>VIKRAM MOHANTY</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/shivam.jpg">
<figcaption><B>SHIVAM VATS</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/rahul_singh.jpg">
<figcaption><B>RAHUL SINGH</b></figcaption>
</figure>
</div>
</div>
<div class="row">
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/divesh.jpg">
<figcaption><B>DIVESH</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/satyesh.jpg">
<figcaption><B>SATYESH MUNDRA</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/shaswat.jpg">
<figcaption><B>SHASHWAT Datta</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/vasudha.jpg">
<figcaption><B>VASUDHA TODI</b></figcaption>
</figure>
</div>
</div>
<br><br><hr />
<h3>KEY FACTS</h3>
<ul class="keyfac">
<li> Probabilistic Robotics Theory</li>
<li>Noise reduction of sensor data</li>
<li>Use of position tracking sensors like GPS,IMU,Wheel Encoders</li>
<li>Use of Filters like KF,EKF</li>
<li>Modelling of a robotic system</li>
<li>Position tracking of a robot</li>
<li>Mapping and SLAM technique</li>
<li>Fusion of Sensor data</li>
<li>Mapping of dynamic environment.</li>
<li>Exploration of Complex Environment</li>
<li>Navigation and Odometry</li>
<li>Robot Kinematics</li>
<li>Autonomous acquisition of information</li>
<li>Knowledge of iterative methods</li>
</ul>
</div>
</div>
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<img src="images/Research/Control_System/controlsystem.jpg" alt="">
<a class="overlay" data-toggle="modal" data-target="#project-4">
</a>
</div>
<a class="btn btn-default btn-block" data-toggle="modal" data-target="#project-4">CONTROL SYSTEMS</a>
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<h3> Introduction </h3>
<p>
Layers of control systems are required to let the robot operate at increasing level of competancy.
Many higher level control system as well as lower level control systems are applied together to
get a robust and flexible Robot Control System.
</p>
<h3>What is Control System and PID Controller??</h3>
<p>
A control system is a device that manages, commands, directs or regulates the behavior of other device
or system.There are two common classes of control systems, open loop control systems and closed loop
control systems.
<br>
PID Controller is one of the most popular and simplest controller widely used.It is a control loop feedback
mechanism (controller) that calculates an error value as the difference between a measured process variable
and a desired setpoint. The controller attempts to minimize the error by adjusting the process through use
of a manipulated variable.
<br>
</p>
<h3>Our Approach</h3>
<p>
We are using Roboteq MDC2230 in Eklavya 3.0 which has a built in PID control loop with seperate gains for
each channel and precise speed and position control and we need to set the values of K(p),K(i) and K(d).We had
used a microcontroller with PID controller in Eklavya 2.0 as backup in case of any malfunctioning with roboteq.
</p>
<br>
<br>
<h3>Future Goals</h3>
We are working on prototype for Self-Driving Car for now. Self-Driving Car consist of complex control system in
which simultaneous control of steering, brake,throttle and cruise control is needed. Also path following
integrated with machine learning algorithms will be implemented so that our SDC can learn through human experience
for better drive.
<h3> Team Members</h3>
<div class="row">
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/ayush.jpg">
<figcaption><B>AYUSH PANDEY</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/kawaljeet.jpg">
<figcaption><B>KAWALJEET KUMAR</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/manuj.jpg">
<figcaption><B>MANUJ AGRAWAL</b></figcaption>
</figure>
</div>
<div class="col-md-3 col-sm-6">
<figure>
<img style="width:250px" alt="The Pulpit Rock" src="images/Team Members/sidJ.jpg">
<figcaption><B>SIDDHARTH JHA</b></figcaption>
</figure>
</div>
</div>