This repository contains all work completed during the Robotics course at IIITN. Throughout the course, various tasks were undertaken, exploring different aspects of robotics, including reinforcement learning, robot modeling, object detection, trajectory planning/analysis, and SLAM (with Webots).
This task focused on building a robot model in Webots and training a Reinforcement Learning (RL) agent from scratch. The objective was for the robot to navigate through an environment, avoiding obstacles and reaching a predefined goal.
This task involved applying deep reinforcement learning to balance a cart-pole system in Webots. The goal was to use Deepbots for training an RL agent to stabilize the pole on top of the cart.
In this task, a robot model was built in Webots, focusing on designing the mechanical structure and dynamics of the robot to perform tasks in a simulated environment. This model served as the foundation for further exploration in subsequent tasks.
For this task, the robot model was extended by adding a camera and developing a color detection system to identify and interact with objects in the robot’s environment. The task also involved programming the robot to navigate toward and manipulate specific objects based on their colors.
Task 5: Use RVC Toolbox in MATLAB to Make Manipulators and Robotic Arms for Trajectory Analysis/Planning
This task utilized the RVC (Robotic Vision and Control) toolbox in MATLAB to model robotic manipulators and arms. The focus was on performing trajectory analysis and planning to simulate the movement of robotic arms and optimize path planning for tasks such as pick-and-place operations.
This task involved learning to integrate ROS2 (Foxy) with Webots for real-time robotic simulations. The focus was on implementing Simultaneous Localization and Mapping (SLAM) to enable the robot to construct a map of an unknown environment while simultaneously keeping track of its location within that map.
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