Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

Implementation of bug1and bug2 algorithms

Python implementation of Bug algorithms on 3-wheel omnidirectional and Webots simulation.

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

Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

Implementation of wall-follow , bug0 algorithms

MATLAB implementation of control and navigation algorithms for mobile robots

Implementation of Bug's algorithms for mobile robots in V-REP simulator

Implementation of Bug Algorithms and Basic Forwards / Inverse Kinematics Equations.

Code for implementing localization, planning and control in an autonomous car using esp-32 microcontroller.

A collection of motion planning projects in Python 3 and YAML

OmniSeekers is a robotic platform for search-and-rescue tasks, featuring a swarm of affordable omnidirectional robots. It combines Bug Algorithms and Bluetooth-based communication to explore indoor environments without GPS or SLAM, offering efficient, centralized control and modular development.

Navigate robots intelligently with MATLAB! Bypass obstacles and find direct paths effortlessly. Visualize every step of your robot's journey. Perfect for beginners and experts alike. Upgrade your robot's intelligence now!

TASK1 for EYRC 2020

Implementation of "Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator". We present a global path planning method in this project which is based on an LSTM model that predicts safe paths for the desired start and goal points in an environment with polygonal obstacles, using a new loss function (MSE-NER).

A Bresenham's line based global path planning algorithm. A recursive path planning algorithm was developed that operates on the grid maps represented by a masked array and solves potential looping problems using a state machine-based loop breaking mechanism.