rongrong1314's repositories
Informative-Path-Planning-1
The Informative Path Planning problem is to find some path that maximizes information gain subject to a set of constraints. In this project we are trying to learn some arbitrary 2D field (a temperature or topography map).
PythonRobotics
Python sample codes for robotics algorithms.
AerialRobotics
Simulate the path planning and trajectory planning of quadrotors/UAVs.
awesome-monte-carlo-tree-search-papers
A curated list of Monte Carlo tree search papers with implementations.
DRL_graph_exploration
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
Fixed-wing-simulator
A fixed-wing simulator for path following
FS-SLAM
Implementation of the accepted paper "2D Laser SLAM with Closed Shape Features: Fourier Series Parameterization and Submap Joining"
FUEL
An Efficient Framework for Fast UAV Exploration
gbplanner_ros
Graph-based Exploration Planner for Subterranean Environments
gpsearch
Bayesian optimization and active learning with likelihood-weighted acquisition functions
gpsearch-1
Bayesian optimization and active learning with likelihood-weighted acquisition functions
hdcp_planning
RAL/IROS 2020: Online Hex-Decomposed Coverage Planning (HDCP) Algorithm
HouseExpo
HouseExpo: A Large-scale 2D Indoor Layout Dataset
lidar-montecarlo-pathplanning
CS 598 Final Project: Self Driving using Path Planning with Monte Carlo Tree Search on Lidar Data
livox_mapping
A mapping package for Livox LiDARs
mbplanner_ros
Motion-primitives Based Planner for Fast & Agile Exploration
MCTS-NNET
Monte Carlo Tree Search with Reinforcement Learning for Motion Planning
mGP_planner
Online Informative Path Planning for Active Information Gathering of a 3D Surface
pareto-mcts
Python demo for the paper "Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning".
ros_autonomous_slam
ROS package which uses the Navigation Stack to autonomously explore an unknown environment with help of GMAPPING and constructs a map of the explored environment. Finally, a path planning algorithm from the Navigation stack is used in the newly generated map to reach the goal. The Gazebo simulator is used for the simulation of the Turtlebot3 Waffle Pi robot. Various algorithms have been integrated for Autonomously exploring the region and constructing the map with help of the 360-degree Lidar sensor. Different environments can be swapped within launch files to generate a map of the environment.
rrdt
Rapidly-exploring Random disjointed-Trees for Motion Planning
stoec_planner
Codes for the IROS2017 E. Ayvali ,H. Salman, H. Choset, "Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization"
tmplanner
Terrain monitoring planner
trajectory_optimization
Viewpoints optimization based on point cloud input to maximize an environment coverage objective
UMich-ROB-530-public
UMich 500-Level Mobile Robotics Course
usv_sim_lsa
Unmanned Surface Vehicle simulation on Gazebo with water current and winds