This project is mainly about testing different path planning techniques in a certain world full of obstacles and how turtlebot3 managed to get to the goal position. It tackles 3 path planning technquies which are ( Artificial potential field (APF), Breadth first search (BFS), A*).

Implementation of RRT (Rapidly-Exploring Random Tree) path planning algorithm

Python implementation of Bug2 algorithm to navigate a quadcopter/multirotor in the AirSim simulator.

Landmark based FastSLAM implementation followed by global path planning for a small four wheeled robot

Software which runs in the Jetson Nano board within the wheelchair, performing functions like Depth Estimation and Path Planning.

Using Dijkstra Path Planning algorithm to solve the shortest path problem for a mobile robot.

Triangular Geometrised RRT* algorithm to decrease the time and cost of the path computed by RRT*

This repository has a simulator UI for path planning of robot using Model Reference Adaptive Control Algorithm (MRAC) to control the robot's trajectory by following the reference trajectory (reference model).

This repository contains ROS-based implementations of PID and Pure Pursuit controllers for mobile robots. The PID controller adjusts the steering angle to minimize lateral error, while the Pure Pursuit controller calculates the required steering angle to follow a predefined path.

This algorithm generates a GPS route, enabling drones to autonomously scan entire areas by following this route. The parameter 'R' represents the drone's domain radius, ensuring efficient coverage.

Explore a map to find a path from start to goal

Repository for Fetch Robotics simulation and POCs