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.

Trajectory Planner in Multi-Agent and Dynamic Environments

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

Detection and Tracking of Moving Objects (DATMO) using sensor_msgs/Lidar.

Open-source Autonomy Software in Rust-lang using gRPC for the Roomba series robot vacuum cleaners. Under development.

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

Perception-Aware Trajectory Planner in Dynamic Environments

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

Several controllers to move Ridgeback mobile-robot and UR5 robotic-arm.

ROS workspace that creates a trajectory for a UAV to follow passing through a set of given waypoints and avoiding a set of given cylindrical obstacles.

Multi-agent motion planning packages (HDSM method)

Spherical Vector-based Particle Swarm Optimization

Decentralized Multiagent Trajectory Planner Robust to Communication Delay

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

🤖 A motion planning MATLAB & V-rep implementation for the KUKA LBR iiwa robotic arm, performing null-space reconfiguration for obstacle avoidance.

LIDAR based Obstacle Avoidance with Reinforcement Learning

Model Predictive Control for a quadrotor in static and dynamic environments