Anubhav Singh

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.

  • Github
  • Stack Overflow
  • Robots
  • Experience
  • Education
  • Patents
  • Projects
  • Certifications

    • Robots:

    6-DOF Robotic Arm

    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 MoveIt motion planning framework to orchestrate precise motion planning and manipulation tasks for the robotic arm. Augmenting this capability, OctoMap was utilized to construct an intricate 3D representation of the environment, enabling meticulous collision checking throughout the motion planning process. Additionally, the ros_control framework was used to execute real-time control of the robotic arm.

    5-DOF Robotic Arm

    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.

    UV Bot Controller

    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.

    Low-cost Line Follower Robot

    I worked on a low-cost line following robot controlled by Raspberry Pi 3 Model B, equipped with a logitech camera.

    3-DOF Robotic Arm

    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 patent 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.

    2-DOF Robotic Arm powered by Hydraulics

    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.

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    Experience:

    • Orangewood Labs

      Research and Development Engineer, AI & Robotics (Oct 2018 - Dec 2021)

    • Lumiq.ai

      AI Engineer Intern (Feb 2018 - Aug 2018)

    Education:

    Patents:

    • System and/or method for error compensation in mechanical transmissions

      • Abhinav Kumar, Aditya Bhatia, Akash Bansal, Anubhav Singh, Ashutosh Prakash, Aman Malhotra, Harshit Gaur, Prasang Srivasatava, Ashish Chauhan
      Orangewoodlabs Inc., Publication Number WO/2021/211578, Publication Date 21.10.2021. (LINK)

      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.

    Projects:



    Github contributions in the last year

    anubhav's Github contribution chart

    Object Segmentation and clustering in realtime 3D Point Clouds using PCL

    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.

    Robotic Arm Software Controller

    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.

    6-DOF Robotic Arm Simulation

    In this project, I implemented a pick-and-place action using a 6-DOF robotic arm, leveraging the octomap 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.

    Web-based Clickstream Data Analysis using ML

    This constituted my undergraduate thesis during the final year of my bachelor's program.

    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.

    Certifications: