[UavNetSim-v1]: A Python-based simulation platform for designing and testing communication protocols and control algorithms in UAV swarm.

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*).

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

A fully visualized implementation of the Dynamic Window Approach (DWA) in Python using Pygame. Simulate and visualize obstacle avoidance and goal-reaching for mobile robots in 2D space — perfect for robotics beginners, path planning researchers, and AI robotics students!

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

A modular SLAM system combining Particle Filter-based localization, Occupancy Grid Mapping (OGM), Dynamic Window Approach (DWA) for real-time obstacle avoidance, and D* Lite for global path replanning. This project integrates both probabilistic mapping and real-time motion planning, suitable for research and educational use in robotics.

Path Planning for Intelligent Mobile Robots A comprehensive framework for advanced path planning algorithms used in autonomous mobile robots.

Explore a map to find a path from start to goal

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

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.

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

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.

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.

Hazard-aware A* pathfinding with a safety-score wavefront, C++ demo + Python prototype, designed for embedded MCU deployment.

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).