Fast, Attemptable Route Planner for Navigation in Known and Unknown Environments

Overview

FAR Planner uses a dynamically updated visibility graph for fast replanning. The planner models the environment with polygons and builds a global visibility graph along with the navigation. The planner is capable of handling both known and unknown environments. In a known environment, paths are planned based on a prior map. In an unknown environment, multiple paths are attempted to guide the vehicle to goal based on the environment observed during the navigation. When dynamic obstacles are present, FAR Planner disconnects visibility edges blocked by the dynamic obstacles and reconnects them after regaining visibility. The software implementation uses two CPU threads - one for dynamically updating the visibility graph using ~20% of the thread and the other for path search that can find a path within 3ms, as evaluated on an i7 computer.

FAR Planner was used by the CMU-OSU Team in attending DARPA Subterranean Challenge. In the final competition which took place in Louisville Mega Cavern, KY, the team's robots conducted the most complete traversing and mapping across the site (26 out of 28 sectors) among all teams, winning a "Most Sectors Explored Award".

A video showing functionalities of FAR Planner is available.

Method

Usage

The repository has been tested in Ubuntu 18.04 with ROS Melodic and Ubuntu 20.04 with ROS Noetic. Follow instructions in Autonomous Exploration Development Environment to setup the development environment. Make sure to checkout the branch that matches the computer setup, compile, and download the simulation environments.

To setup FAR Planner, clone the repository.

git clone https://github.com/MichaelFYang/far_planner

In a terminal, go to the folder and compile.

cd far_planner
catkin_make

To run the code, go to the development environment folder in a terminal, source the ROS workspace, and launch.

source devel/setup.sh
roslaunch vehicle_simulator system_indoor.launch

In another terminal, go to the FAR Planner folder, source the ROS workspace, and launch.

source devel/setup.sh
roslaunch far_planner far_planner.launch

Now, users can send a goal by pressing the 'Goalpoint' button in RVIZ and then clicking a point to set the goal. The vehicle will navigate to the goal and build a visibility graph (in cyan) along the way. Areas covered by the visibility graph become free space. When navigating in free space, the planner uses the built visibility graph, and when navigating in unknown space, the planner attempts to discover a way to the goal. By pressing the 'Reset Visibility Graph' button, the planner will reinitialize the visibility graph. By unchecking the 'Planning Attemptable' checkbox, the planner will first try to find a path through the free space. The path will show in green. If such a path does not exist, the planner will consider unknown space together. The path will show in blue. By unchecking the 'Update Visibility Graph' checkbox, the planner will stop updating the visibility graph. To read/save the visibility graph from/to a file, press the 'Read'/'Save' button. An example visibility graph file for indoor environment is available at 'src/far_planner/data/indoor.vgh'.

Indoor

Anytime during the navigation, users can use the control panel to navigate the vehicle by clicking the in the black box. The system will switch to smart joystick mode - the vehicle tries to follow the virtual joystick command and avoid collisions at the same time. To resume FAR planner navigation, press the 'Resume Navigation to Goal' button or use the 'Goalpoint' button to set a new goal. Note that users can use a PS3/4 or Xbox controller instead of the virtual joystick. For more information, please refer to our development environment page.

ControlPanel     PS3 Controller

To launch with a different environment, use the command lines below and replace '<environment>' with one of the environment names in the development environment, i.e. 'campus', 'indoor', 'garage', 'tunnel', and 'forest'.

roslaunch vehicle_simulator system_<environment>.launch
roslaunch far_planner far_planner.launch

To run FAR Planner in a Matterport3D environment, follow instructions on the development environment page to setup the Matterport3D environment. Then, use the command lines below to launch the system and FAR Planner.

roslaunch vehicle_simulator system_matterport.launch
roslaunch far_planner far_planner.launch config:=matterport

Matterport

Configuration

FAR planner settings are kept in default.yaml in the 'src/far_planner/config' folder. For Matterport3D environments, the settings are in matterport.yaml in the same folder.

  • is_static_env (default: true) - set to false if the environment contains dynamic obstacles.

Todo

  • The current implementation does not support multi-floor environments. The environment can be 3D but needs to be single floored. An upgrade is planned for multi-floor environment support.

Reference

  • F. Yang, C. Cao, H. Zhu, J. Oh, and J. Zhang. FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update. Submitted in 2021.

Author

Fan Yang ([email protected])

Credit

Eigen: a lightweight C++ template library for linear algebra.

You might also like...
(Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

NeRF--: Neural Radiance Fields Without Known Camera Parameters Project Page | Arxiv | Colab Notebook | Data Zirui Wang¹, Shangzhe Wu², Weidi Xie², Min

Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters
Unofficial & improved implementation of NeRF--: Neural Radiance Fields Without Known Camera Parameters

[Unofficial code-base] NeRF--: Neural Radiance Fields Without Known Camera Parameters [ Project | Paper | Official code base ] ⬅️ Thanks the original

A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)

A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)

Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Updated Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol. Introduction This balenaCloud (previously

Episodic Transformer (E.T.) is a novel attention-based architecture for vision-and-language navigation. E.T. is based on a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions.
Code and data of the Fine-Grained R2R Dataset proposed in paper Sub-Instruction Aware Vision-and-Language Navigation

Fine-Grained R2R Code and data of the Fine-Grained R2R Dataset proposed in the EMNLP2020 paper Sub-Instruction Aware Vision-and-Language Navigation. C

An open source bike computer based on  Raspberry Pi Zero (W, WH) with GPS and ANT+. Including offline map and navigation.
An open source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+. Including offline map and navigation.

Pi Zero Bikecomputer An open-source bike computer based on Raspberry Pi Zero (W, WH) with GPS and ANT+ https://github.com/hishizuka/pizero_bikecompute

PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

Code for the paper "Improving Vision-and-Language Navigation with Image-Text Pairs from the Web" (ECCV 2020)

Improving Vision-and-Language Navigation with Image-Text Pairs from the Web Arjun Majumdar, Ayush Shrivastava, Stefan Lee, Peter Anderson, Devi Parikh

Comments
  • what  means IsAPillarPolygon in the contour_graph.cpp

    what means IsAPillarPolygon in the contour_graph.cpp

    hey my dear author , in the contour_graph.cpp i saw a bool func that IsAPillarPolygon , but i dont know what the func is done what means PillarPolygon literally?

    opened by Leeable 2
  • problem of saving Vsibility Graph

    problem of saving Vsibility Graph

    When I am using the function of reading or saving Visibility Graph There are only a part of Vsibility Graph can be saved. The main path of the vehicle can be saved but the other line is missing.

    The picture of real path which I want to save Screenshot from 2021-11-15 07-53-01

    The picture of path when I am reading Screenshot from 2021-11-15 07-54-40

    question 
    opened by overevil 2
  • How to use far_planner in a known map environment

    How to use far_planner in a known map environment

    Thank you very much for your contribution. I want to save the 3D map in PCD format after building the map. The next time I directly import the map, I can use far_planner for planning only by positioning. How should I achieve this? Is it necessary to use far_planner to run the map and save the visibility graph first?

    opened by W0WSS 0
Owner
Fan Yang
Fan Yang
Multi-robot collaborative exploration and mapping through Voronoi partition and DRL in unknown environment

Voronoi Multi_Robot Collaborate Exploration Introduction In the unknown environment, the cooperative exploration of multiple robots is completed by Vo

PeaceWord 6 Nov 22, 2022
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

Graph-Based Local Trajectory Planner The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visuali

TUM - Institute of Automotive Technology 160 Jan 4, 2023
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation (CoRL 2021)

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation [Project website] [Paper] This project is a PyTorch i

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 6 Feb 28, 2022
Convert Mission Planner (ArduCopter) Waypoint Missions to Litchi CSV Format to execute on DJI Drones

Mission Planner to Litchi Convert Mission Planner (ArduCopter) Waypoint Surveys to Litchi CSV Format to execute on DJI Drones Litchi doesn't support S

Yaros 24 Dec 9, 2022
Neural Factorization of Shape and Reflectance Under An Unknown Illumination

NeRFactor [Paper] [Video] [Project] This is the authors' code release for: NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown I

Google 283 Jan 4, 2023
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

Ursa Zrimsek 2 Dec 14, 2022
Towards Debiasing NLU Models from Unknown Biases

Towards Debiasing NLU Models from Unknown Biases Abstract: NLU models often exploit biased features to achieve high dataset-specific performance witho

Ubiquitous Knowledge Processing Lab 22 Jun 14, 2022
Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

null 68 Dec 14, 2022
CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary.

CUP-DNN CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary. The model was trained on the expre

null 1 Oct 27, 2021
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

Marcel R. 349 Aug 6, 2022