Student Details:
- Pursuing BS (4 year) in Mathematics and Computing
- Member of the Software Team
- A Sophomore as of Aug 2024
Ongoing
Smaller projects done till now for understanding the basics:
- Implemented an Optimal Policy generator by iteratively solving the Bellman Equation for a 2D grid environment, specifically one where each square is either an obstacle or is one with a reward for it. Link
Completed
Reference Paper: Minimum curvature trajectory planning and control for an autonomous race car
- Link to Implementation: AGV-2023-Planning-Teams/minimum_curvature_planner
- Calculated the QP problem that needs to be passed to a solver
- Coded a setup to visualize output trajectories
- Created a Powerpoint presentation to describe the algorithm
Ongoing
Course Link: Autoware course hosted by Apex.AI
- Documentation Link: GitHub link to my documentation
Completed
Task Link: tharun-selvam/sim_task_agv
- This project consists of multiple GitHub repositories edited by myself.
- github.com/real-Sandip-Das/sim_task_agv (Main repository, contains explanations, instructions and links to other packages)
- real-Sandip-Das/aruco_opencv_to_cartographer_landmark (a custom ROS Package for translating Aruco Detection messages to a format useful for Google Cartographer SLAM Nodes)
- I was required to detect Aruco markers from the camera's topic, but initially I couldn't find a package that was able to do that, so I decided to select a specific package, clone its source locally and debug it.
- fictionlab/ros_aruco_opencv (I debugged its
noetic
branch and made a Bug Fix in this repository: Pull Request for my Bug Fix and then used this in my project) - Docker: After completing the project, I uploaded the Docker Image for an easier experience of potential evaluators: realsandipdas/sim_task_agv/
Edited and Improved
Link to Paper: Conflict-based search for optimal multi-agent pathfinding
- Task based on implementation of an algorithm described in a paper
- Although this task was assigned during the selection process, I have been polishing my solution (simply, so that I can show this to other people as a project in general)
- GitHub Repository: real-Sandip-Das/Conflict-Based-Search/tree/converting_in_cpp
Completed
Google Doc with Problem statement and relevant details
- In short, contains a publisher and a subscriber node, both written in C++, where things like the published dummy string, topic(for publishing/subscribing to), queue size of subscriber node are customizable using ROS Parameters
- GitHub Repository: real-Sandip-Das/pub_sub_comm
- Documentation: contained in the GitHub repository's
README.md
file
Completed
Google Doc with Problem statement and relevant details
- This task involves a light amount of mathematics, so I've used LaTeX(MathJax) to document the mathematical details (calculations)
- The entire C++ Source code is documented(along with the mathematical details) using Doxygen (proper instructions are given in the repository's
README.md
) - GitHub Repository: real-Sandip-Das/noisy_turtle_playback
- Documentation: contained in the GitHub repository's
README.md
file - I know I could just use a library function to generate samples of a Bi-variate Normal Distribution, but I thought it would be fun to involve mathematics with proper documentation in this task.
Completed
Course Link: Programming for Robotics - ROS
- Documentation: real-Sandip-Das/AGV-ROS-task-doc
- GitHub Repositories: smb_highlevel_controller, smb_highlevel_controller/tree/Exercise2(branch)
Completed
Task Link: Cath3dr4l/AGV-git-task
- Documentation: real-Sandip-Das/AGV-git-task-doc
- GitHub Repository: real-Sandip-Das/AGV-git-task