Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

Multi-Purpose MPC for Reference Path Tracking, Time-Optimal Driving and Obstacle Avoidance

A* Search Algorithm with an Additional Time Dimension to Deal with Dynamic Obstacles

A toolkit for testing control and planning algorithm for car racing.

Implementation of the D* lite algorithm in Python for "Improved Fast Replanning for Robot Navigation in Unknown Terrain"

A goal-driven autonomous exploration through deep reinforcement learning (ICRA 2022) system that combines reactive and planned robot navigation in unknown environments

Deep Reinforcement Learning for Fixed-Wing Flight Control with Deep Q-Network

Model Predictive Control for a quadrotor in static and dynamic environments

Control Barrier Functions for Quadrotors, standalone with dynamics simulator, nominal controllers.

Safety-critical controllers for single/multi robotic navigation: CBF-QP, MPC-CBF, and etc.

The underwater robot obstacle avoidance project with the method of deep reinforcement learning

An environment for an obstacle avoidance task

Using DDPG and ConvLSTM to control a drone to avoid obstacle in AirSim

Mobile Robot Path Planning and Obstacle Avoidance Using PSO in Python

[Tensorflow] High Correlation Data Removing Method for Deep Reinforcement Learning in Obstacle Avoidance and Path Planning

RRT*-MPC path planning for spacecraft navigation in dynamic environment. Graded project for the ETH course "Planning and Decision Making for Autonomous Robots".

A cool project in which we do path planning in an environment with moving obstacles and large scale fixed obstacles. We use two different representations of the world to handle the fixed and moving obstacles.

Bio-inspired run and tumble and obstacle avoidance behaviour on Crazyflie 2.1

Quadruped Trajectory Optimization Stack (QTOS) is an optimization framework for legged locomotion that autonomously generates full-body trajectory plans across challenging terrains.