Lei He's repositories
UAV_Navigation_DRL_AirSim
This is a new repo used for training UAV navigation (local path planning) policy using DRL methods.
px4_avoidance_airsim
Integration of Fast-Planner and PX4-Avoidance with AirSim in UE4 environment
UAV-navigation-papers
Paper list for UAV navigation in unknown complex environment
gym_airsim_multirotor
gym_airsim_multirotor is a customized OpenAI gym environement for AimSim.
gym_gazebo_px4
OpenAI gym environment for px4 sitl
Fixedwing-Airsim
Combines JSBSim and Airsim with a python module to simulate a fixedwing
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
td3fd_for_chuyao
TD3fD framework
web-lei-hugo-academic
Personal website uisng Hugo academic
Adaptive_Sliding_Mode_Control_of_Aerial_Manipulator
Adaptive Sliding Mode Control
eagle_mpc_ros
EagleMPC-ROS contains several packages to run EagleMPC within a ROS environment
Fast-Planner
A Robust and Efficient Trajectory Planner for Quadrotors
gcbfplus
Jax Official Implementation of Paper: S Zhang*, Oswin So*, K Garg, C Fan: "GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control".
motion-planning-practice
Fast Python motion planning algorithm implementations with demos in pybullet
my-eagle-mpc
EagleMPC is a model predictive control & optimal control library for unmanned aerial manipulators (UAMs)
my-ego-planner-swarm
An efficient single/multi-agent trajectory planner for multicopters.
my_dso
Direct Sparse Odometry with comment
my_evo
Python package for the evaluation of odometry and SLAM
my_spline_vio
Continuous-Time Spline Visual-Inertial Odometry
OpenMV-scripts
Some scipts file for OpenMV using MicroPython
PX4-Autopilot
Fork of PX4 Autopilot Software. Used for personal development.
PX4-Avoidance
PX4 avoidance ROS node for obstacle detection and avoidance.
qgroundcontrol
Cross-platform ground control station for drones (Android, iOS, Mac OS, Linux, Windows)
stable-baselines
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms