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<!DOCTYPE html>
<html lang="en-US">
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<title>Anubhav Singh</title>
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<h3>About me</h3>
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<div class="col-md-12">
<div class="post-content">
<h1>Anubhav Singh</h1>
<p class="l_desc">
<font size="3"> As an avid AI and robotics enthusiast, I am deeply committed to
addressing cutting-edge challenges within the robotics domain, with a profound
focus on shaping our collective future sustainably. My passion is particularly
centered around an array of intricate robotics facets, encompassing robot
dynamics, kinematics, motion planning, control systems, computer vision,
reinforcement learning, and robot navigation. I aspire to contribute
meaningfully to the evolving landscape of robotics through both
theoretical understanding and hands-on application.</fontsize><br />
<!--<font size="3"> I aspire to contribute
meaningfully to the evolving landscape of robotics through both
theoretical understanding and hands-on application.</fontsize><br />-->
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<li>
<a title="Github" href="https://github.com/anubhav1772" target="_blank">Github</a>
</li>
<li>
<a title="Stack Overflow" href="https://stackoverflow.com/users/5810950/anubhav-singh?tab=profile" target="_blank">Stack Overflow</a>
</li>
<li>
<a href="#rob">Robots</a>
</li>
<li>
<a href="#exp">Experience</a>
</li>
<li>
<a href="#edu">Education</a>
</li>
<li>
<a href="#pat">Patents</a>
</li>
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<li>
<a href="#res">Projects</a>
</li>
<li>
<a href="#cert">Certifications</a>
</li>
<p> <a name="stackoverflow"></a></p>
<!-- <h3>
<strong>Stack Overflow</strong>:<strong></strong><br />
</h3> -->
<!-- <a href="https://stackoverflow.com/users/5810950/anubhav-singh?tab=profile"><img src="https://stackoverflow.com/users/flair/5810950.png" width="208" height="58" alt="profile for Anubhav Singh at Stack Overflow, Q&A for professional and enthusiast programmers" title="profile for Anubhav Singh at Stack Overflow, Q&A for professional and enthusiast programmers"></a>
<hr style="borber: 1px black solid;" /> -->
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<p> <a name="rob"></a></p>
<h2>
<strong>Robots</strong>:<strong></strong><br />
</h2>
<div class="slideshow-container">
<!-- Slides -->
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<!-- <img class="l_thumbnail img-thumbnail img-responsive"
src="robots/6dof.jpg" data-recalc-dims="1"/> -->
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<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/6dof.jpg" data-recalc-dims="1"/>
</div>
<div class="slide-description">
<h3 class="l_title">
<a href="https://github.com/anubhav1772/6-dof-robotic-arm" title="6DOF robotic arm" target="_blank">6-DOF Robotic Arm</a><br />
</h3>
<p class="l_desc">
A ROS-enabled 6-DOF robotic arm built using RMD series servo motor system equipped
with CAN bus protocol for real-time data transmission and communication purposes.
We employed the <I>MoveIt</I> motion planning framework to orchestrate precise motion
planning and manipulation tasks for the robotic arm. Augmenting this capability,
<I>OctoMap</I> was utilized to construct an intricate 3D representation of the environment,
enabling meticulous collision checking throughout the motion planning process.
Additionally, the <I>ros_control</I> framework was used to execute real-time control
of the robotic arm.
</p>
</div>
</div>
<div class="slide">
<div class="slide-image">
<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/5dof.jpg" data-recalc-dims="1"/>
</div>
<div class="slide-description">
<h3 class="l_title">
5-DOF Robotic Arm<br />
</h3>
<p class="l_desc">
I worked on the software controller of this 5-DOF robotic arm. We
made a controller using python which sends commands to the robot
actuators via arduino microcontroller. I didn't work on this arm for
long since my company switched to the 6-DOF robotic arm.
</p>
</div>
</div>
<div class="slide">
<div class="slide-image">
<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/uvbot.jpg"
data-recalc-dims="1" />
</div>
<div class="slide-description">
<h3 class="l_title">
UV Bot Controller<br />
</h3>
<p class="l_desc">
I designed and implemented a mobile application to seamlessly control a UV bot, utilizing ESP32 and BLE modules. The app provides dual connectivity options, allowing communication with the robot via Bluetooth or WiFi. Furthermore, the UV bot can be remotely operated from any location. To facilitate secure communication, Ngrok was employed, establishing secure tunnels between the local development server and the
internet.
</p>
</div>
</div>
<div class="slide">
<div class="slide-image">
<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/linefollower.JPG" data-recalc-dims="1"/>
</div>
<div class="slide-description">
<h3 class="l_title">
Low-cost Line Follower Robot<br />
</h3>
<p class="l_desc">
I worked on a low-cost line following robot controlled by
Raspberry Pi 3 Model B, equipped with a logitech camera.
</p>
</div>
</div>
<div class="slide">
<div class="slide-image">
<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/3dof.jpg" data-recalc-dims="1"/>
</div>
<div class="slide-description">
<h3 class="l_title">
3-DOF Robotic Arm<br />
</h3>
<p class="l_desc">
I worked with a 3-DOF robotic arm in 2018. I worked on
the robot controller software and built an ML model to
reduce the backlash error and improve the repeatability
of the robotic arm. This ML work, later on, became the
basis for the <a href="https://patents.google.com/patent/US20220001533A1/en">patent</a>
we applied with the USPTO. I spearheaded the project for
an year and half, designed system to collect data and
integrated the ML model to the system.
</p>
</div>
</div>
<div class="slide">
<div class="slide-image">
<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/2dofhydraulicarm.JPG" data-recalc-dims="1"/>
</div>
<div class="slide-description">
<h3 class="l_title">
2-DOF Robotic Arm powered by Hydraulics<br />
</h3>
<p class="l_desc">
I developed a 2-DOF robotic arm for my college robotic
competition in 2014. Both of the joints were controlled
by hydraulics and the four wheels were controlled by
DC motors running on AC supply using transformer and
transistor rectifier.
</p>
</div>
</div>
<!-- Navigation Arrows -->
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</div>
<hr style="borber: 1px black solid;" />
<p> <a name="exp"></a></p>
<h2>
<strong>Experience</strong>:<strong></strong><br />
</h2>
<ul>
<li>
<h5 class="exp_title">
<a href="https://orangewood.co/" title="Orangewood Labs" target="_blank"><strong>Orangewood Labs</strong></a><br />
</h5>
<p class="exp_desc">
<I>Research and Development Engineer, AI & Robotics</I> (Oct 2018 - Dec 2021)
</p>
</li>
</ul>
<ul>
<li>
<h5 class="exp_title">
<a href="https://lumiq.ai/" title="Lumiq.ai" target="_blank"><strong>Lumiq.ai</strong></a><br />
</h5>
<p class="exp_desc">
<I>AI Engineer Intern</I> (Feb 2018 - Aug 2018)
</p>
</li>
</ul>
<hr />
<p> <a name="edu"></a></p>
<h2>
<strong>Education</strong>:<strong></strong><br />
</h2>
<ul>
<li>
<strong>M.Sc. in Robotics and AI (Mechatronics and Robotics)</strong>, <a href="https://en.itmo.ru/">National Research University ITMO</a>, (2024 - Present)
</li>
</ul>
<ul>
<li>
<strong>Bachelor of Technology (B.Tech) in Computer Science and Engineering</strong>, <a href="https://www.jiit.ac.in/">Jaypee Institute of Information Technology</a>, (2014 - 2018)
</li>
</ul>
<hr />
<p> <a name="pat"></a></p>
<h2>
<strong>Patents:</strong><br />
</h2>
<ul>
<li>
<strong>System and/or method for error compensation in mechanical transmissions</strong>
</li>
<p> Abhinav Kumar, Aditya Bhatia, Akash Bansal, <strong>Anubhav Singh</strong>,
Ashutosh Prakash, Aman Malhotra, Harshit Gaur, Prasang Srivasatava, Ashish Chauhan <br /><I>Orangewoodlabs Inc.</i>, Publication Number
WO/2021/211578, Publication Date 21.10.2021. (<a
href="https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2021211578" title="LINK"
target="_blank">LINK</a>)
</p>
<p class="l_desc"><font size="3">
Mechanical transmission mechanisms can enable systems to gear actuators up (to operate at higher speeds)
or down (to react higher torque loads), but they also can introduce positional error in the system, which
negatively impacts system performance. One example of such error is backlash that occurs in gear systems.
Backlash is generally known in the mechanical systems as an inefficiency that results from clearance or lost
motion in mechanism. Backlash is typically caused by gaps between parts (e.g., the circumferential space
between two meshing gear teeth). Hardware solutions have been proposed to correct positional error in
mechanical transmission systems. An example hardware solution is a harmonic drive specially designed to
reduce or eliminate backlash. While the harmonic drives work well in eliminating error, they are highly complex
mechanical systems and, as a result, are very expensive, sometimes prohibitively so for a commercial product.
Accordingly, there is a need for an improved technique to correct for cumulative errors in mechanical transmission systems.</fontsize>
</p>
</ul>
<hr style="borber: 1px black solid;" />
<p> <a name="res"></a></p>
<h2>
<strong>Projects</strong>:<strong></strong><br />
</h2>
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<br /><br />
<div class="l_item">
<p>Github contributions in the last year<br/><br/>
<img src="http://ghchart.rshah.org/anubhav1772" alt="anubhav's Github contribution chart" height="200" style="width:100%;"/>
</p>
</div>
<div class="l_item">
<div class="left_wrapper">
<!--<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/seg.jpg"
data-recalc-dims="1" />-->
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<h3 class="l_title">
<a href="https://github.com/anubhav1772/robot_vision"
title="Object Segmentation and clustering in realtime 3D Point Clouds using PCL"
target="_blank">Object Segmentation and clustering in realtime 3D Point Clouds using PCL</a><br />
</h3>
<iframe src="https://ghbtns.com/github-btn.html?user=anubhav1772&repo=robot_vision&type=star&count=true&size=large"
frameborder="0"
scrolling="0"
width="170"
height="30"
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height="30"
title="GitHub">
</iframe>
<p class="l_desc">
To get realtime data from d435 Intel realsense camera installed next to the robotic arm
and apply techniques like voxel filtering, statistical outlier removal, RANSAC plane segmentation,
Euclidean clustering, etc. to locate centroid in each segmented object and eventually publish
the detected centroids' coordinated on a ROS publisher of type visualization_msgs/MarkerArray
for pick and place action using suction gripper attached on the end effector of the robotic arm.
</p>
</p>
</div>
</div>
<div class="l_item">
<div class="left_wrapper">
<!--<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/controller.jpg"
data-recalc-dims="1" />-->
<img class="l_thumbnail img-thumbnail img-responsive" id="controller" src="robots/controller.jpg" alt="" />
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<h3 class="l_title">
<a href="https://github.com/anubhav1772/trajectory_planning_gui"
title="Robotic Arm Software Controller"
target="_blank">Robotic Arm Software Controller</a><br />
</h3>
<iframe src="https://ghbtns.com/github-btn.html?user=anubhav1772&repo=trajectory_planning_gui&type=star&count=true&size=large"
frameborder="0"
scrolling="0"
width="170"
height="30"
title="GitHub">
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frameborder="0"
scrolling="0"
width="170"
height="30"
title="GitHub">
</iframe>
<p class="l_desc">
I built a software controller for a 6-DOF robotic arm
using C++ Qt6 framework. The
features included are joint based control, trajectory planning
(joint space as well as cartesian space), hand teaching
(gravity compensation), jogging (joint level, end effector
and joystick-based), robot 3D visualization (similar to RViz),
sensor data visualization, etc. The backend is using ROS and
MoveIt motion planning framework.
</p>
</p>
</div>
</div>
<div class="l_item">
<div class="left_wrapper">
<!--<img class="l_thumbnail img-thumbnail img-responsive"
src="robots/arm_octomap.jpg"
data-recalc-dims="1" />-->
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</div>
<div class="right_wrapper">
<h3 class="l_title">
<a href="https://github.com/anubhav1772/6-dof-robotic-arm"
title="Robotic Arm Simulation"
target="_blank">6-DOF Robotic Arm Simulation</a><br />
</h3>
<iframe src="https://ghbtns.com/github-btn.html?user=anubhav1772&repo=6-dof-robotic-arm&type=star&count=true&size=large"
frameborder="0"
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<p class="l_desc">
In this project, I implemented a pick-and-place action using a 6-DOF robotic arm, leveraging the <a href="https://octomap.github.io/">octomap</a> framework. Data from an RGBD camera sensor is used to build a 3D model of the environment around the robotic arm using the ROS octomap library. MoveIt checks for collisions between the robot and the octomap, and generates a motion plan failure if it detects a potential collision. To successfully perform the pick-and-place task, we need to add the object to the planning scene interface to deactivate collision checking between the gripper and the object. Additionally, we must attach the grasped object to the gripper so that MoveIt considers it during collision checking.
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<h3 class="l_title">
Web-based Clickstream Data Analysis using ML
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<p class="l_desc">This constituted my undergraduate thesis during the final year of
my bachelor's program.
</p>
<p class="l_desc">
In the dynamic landscape of online shopping, users often engage in product
comparisons to make informed decisions. The project involves leveraging
clickstream data, a comprehensive log of user activities on the website, to
enhance the user experience through a sophisticated recommendation system.
The primary goal is to build a robust recommendation system that analyzes
clickstream data, identifies similar user profiles, and tailors product
recommendations based on user behavior. The system aims to streamline
the user's decision-making process by suggesting products from the
baskets of similar users and strategically promoting top-selling items.
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<hr style="borber: 1px black solid;" />
<p> <a name="cert"></a></p>
<h2>
<strong>Certifications</strong>:<strong></strong><br />
</h2>
<ul>
<li>
<a href="https://drive.google.com/file/d/14RZ_k5Vvdi_drmNSGN0ls0_c5O6ctso2/view?usp=sharing">Advanced Robotics </a> - (<a href="https://onlinecourses.nptel.ac.in/noc24_me24/preview">by Indian Institute of Technology Kanpur on NPTEL</a>)
</li>
<li>
<a href="https://coursera.org/share/9bbf450760dbb95cfb7e227a4a364270">Aerial Robotics</a> (by University of Pennsylvania on Coursera)
</li>
<li>
<a href="https://www.udemy.com/certificate/UC-d1b1b2f6-0ef1-4fd8-916e-d1c866657e32/">Industrial Robotics</a> (by Udemy)
</li>
<li>
Reinforcement Learning Specialization (by University of Alberta & Alberta Machine Intelligence Institute on Coursera)
<ul>
<li>
<a href="https://coursera.org/share/508ade200689a81e66bc6fc17f9a56be">Sample-based Learning Methods</a>
</li>
<li>
<a href="https://coursera.org/share/d1ce2d555814d1f36f2af1a184871392">Prediction and Control with Function Approximation</a>
</li>
</ul>
</li>
<li>
Modern Robotics: Mechanics, Planning, and Control Specialization (by Northwestern University on Coursera)
<ul>
<li>
<a href="https://coursera.org/share/d35b9154be5e16c2abba198a07226e0d">Robot Dynamics</a>
</li>
<li>
<a href="https://coursera.org/share/b7af76ca6227b6383e940411318b8e64">Robot Motion Planning and Control</a>
</li>
</ul>
</li>
</ul>
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