Huan2018

SLAM, Inertial navigation, Motion planning (underwater robot, UVMS)

Beijing Institute of Technology; University of technology sydneyBeijing, CHINA; Sydney, AU

Pinned Repositories

autograd
Efficiently computes derivatives of numpy code.
Language:Python00
barrett_hand
ROS package to control the Barrett Hand
Language:Python00
beliefspace
Language:Python00
BeliefSpacePlanning
Implementation of important belief space planning research papers to progress in my research at Biorobotics Lab, CMU
Language:MATLAB63
bsp-ilqg
Belief Space Motion Planning Using iLQG
Language:MATLAB10
cadrl_ros
ROS package for dynamic obstacle avoidance for ground robots trained with deep RL
Language:Python00
cartographer
Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.
Language:C++00
freefloating_gazebo
A Gazebo plugin to simulate underwater vehicles and visualize with UWsim.
Language:C++10
robotRRT
The RRT method with 7-arm redundant manipulators
Language:MATLAB41
Strapdown-inertial-navigation-system
11

Huan2018's Repositories

Huan2018/BeliefSpacePlanning
Implementation of important belief space planning research papers to progress in my research at Biorobotics Lab, CMU
Language:MATLAB63
Huan2018/freefloating_gazebo
A Gazebo plugin to simulate underwater vehicles and visualize with UWsim.
Language:C++10
Huan2018/Strapdown-inertial-navigation-system
11
Huan2018/autograd
Efficiently computes derivatives of numpy code.
Language:Python00
Huan2018/cadrl_ros
ROS package for dynamic obstacle avoidance for ground robots trained with deep RL
Language:Python00
Huan2018/cartographer
Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.
Language:C++00
Huan2018/book
:books: All programming languages books
Huan2018/ENPM661-Planning-for-Autonomous-Robots
Planning is a fundamental capability needed to realize autonomous robots. Planning in the context of autonomous robots is carried out at multiple different levels. At the top level, task planning is performed to identify and sequence the tasks needed to meet mission requirements. At the next level, planning is performed to determine a sequence of motion goals that satisfy individual task goals and constraints. Finally, at the lowest level, trajectory planning is performed to determine actuator actions to realize the motion goals. Different algorithms are used to achieve planning at different levels. This graduate course will introduce planning techniques for realizing autonomous robots. In addition to covering traditional motion planning techniques, this course will emphasize the role of physics in the planning process. This course will also discuss how the planning component is integrated with control component. Mobile robots will be used as examples to illustrate the concepts during this course. However, techniques introduced in the course will be equally applicable to robot manipulators.
Language:MATLAB
Huan2018/fastrack
A ROS implementation of Fast and Safe Tracking (FaSTrack).
Language:C++
Huan2018/gazebo_aerial_manipulation_plugin
Provides interface to command a quadrotor using rpyt commands and if joints are provided, will attach servos to them to control the manipulator using joint angles
Language:C++
Huan2018/gym_ped_sim
A ros gazebo plugin for pedestrians （Raw depth social compliant navigation through GAIL) ICRA 2018
Huan2018/HarmoniousSampling
Harmonious Sampling for Mobile Manipulation Planning, IROS 2019.
Huan2018/HuanYubit.github.io
Huanyu personal blogs
Huan2018/ifopt
An Eigen-based, light-weight C++ Interface to Nonlinear Programming Solvers (Ipopt, Snopt)
Language:C++
Huan2018/LaTeX-template
LaTeX template for BIT thesis
Huan2018/MATLABRobotics
MATLAB sample codes for mobile robot navigation
Language:MATLAB
Huan2018/mav_trajectory_generation
Polynomial trajectory generation and optimization, especially for rotary-wing MAVs.
Language:C++
Huan2018/Mobile-DeepLearning-in-TensorFlow-Lite
The demo about Mobile DeepLearning in TensorFlow Lite
Language:Java
Huan2018/MPNet
Motion Planning Networks
Language:C
Huan2018/nndl.github.io
《神经网络与深度学习》邱锡鹏著 Neural Network and Deep Learning
Huan2018/phd-bibliography
References on Optimal Control, Reinforcement Learning and Motion Planning
Huan2018/Published_Papers_HuanYU
Huan2018/PythonRobotics
Python sample codes for robotics algorithms.
Language:Jupyter Notebook
Huan2018/quadrotor
Quadrotor control, path planning and trajectory optimization
Language:Matlab
Huan2018/rhem_planner
Uncertainty-aware Receding Horizon Exploration and Mapping Planner
Language:C++
Huan2018/rotors_simulator
RotorS is a UAV gazebo simulator
Language:C++
Huan2018/rrbt
Rapidly exploring Random Belief Tree
Language:C
Huan2018/uuv_simulator
Gazebo/ROS packages for underwater robotics simulation
Language:Python
Huan2018/uvms-simulator
A simulation environment of an underwater vehicle-manipulator system implemented in Matlab
Language:Matlab
Huan2018/UVMSmotionplanning
I-AUV(UVMS) motion planning under uncertainty