Stars
Reinforcement learning baseline agent trained with the Actor-critic (A3C) algorithm.
Create pgm map from Gazebo world file for ROS localization. Tested on Ubuntu 20.04, ROS Noetic, Boost 1.71，Gazebo 11
A scout2 robot carrying RS16 lidar/IMU/D435 sensors，running LIO-SAM in a room!
Differential Wheeled Mobile Robot - Nonlinear Model Predictive Control based on ROS
Pure Pursuit Control and SE(2) Planning
Novel reinforcement learning based local planner that accounts for the dynamic constraints of the robot to enable smooth robot trajectories. Reward shaping is done to enable a spatially aware navig…
该节点是为适配autoware.universe自动驾驶框架而编写的ROS2节点，目前仅支持mick_robot_chassis 开源底盘。
[RA-Letter 2023] RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environments
A hierarchical framework for Multi-Robot autonomous exploration inspired by centroids of unknown regions
A platform for executing RRT exploration in ROS Noetic and Ubuntu 20.04LTS
Lightweight ROS local path planner plugin with PSO and LQR algorithms
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Simple and efficient implementation of DQN DDPG TD3 SAC PPO MADDPG MATD3 MASAC MAAC IPPO MAPPO HAPPO MAT MORL
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 g…
[T-IV] Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving
A Python-based lightweight robot simulator designed for navigation, control, and learning
[TRO 2025] NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning.
[RA-Letter 2022] Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards
[T-ITS'23] Sim-to-real goal-oriented mapless autonomous navigation (DRL navigation).
A minimalist environment for decision-making in autonomous driving