[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3352593.3352615acmotherconferencesArticle/Chapter ViewAbstractPublication PagesairConference Proceedingsconference-collections
research-article

Path Planning and Obstacle Avoidance in CG Space of a 10 DOF Rover using RRT

Published: 27 January 2020 Publication History

Abstract

There has been a recent demand for algorithms to plan the motion on 3D terrain for applications in space exploration, rescue and relief, unmanned vehicles, defense applications etc. Conventional path planning algorithms in 2D cannot be used in 3D as the work space cannot be divided into obstacles and free space. A few algorithms have been proposed for articulated rovers in 3D and they all require optimization to find the wheel and ground terrain contact and hence cannot be used in real time. In this paper, a new method is proposed to find the path in the CG space of a 10 DOF rover without the need for optimization. This CG space planning method can operate in real time. The CG space is the collection of possible CG points of the rover on a given terrain that is similar to the C-space in robot motion planning. The terrain geometry used for generating the CG position of rover is obtained using a Microsoft Kinect V2. A multivariable optimization process is used to extract the CG locus data of the rover as a discrete point cloud to generate CG space. Then using RRT* algorithm, the feasible path to reach a goal location from an initial point avoiding obstacles has been found out. During the motion planning for 10 DOF rover, RRT* algorithm directly samples a node from the CG locus data. It searches globally for an optimal path via two optimizing features in the extend function. Simulations on different types of terrains with different obstacle shapes show the usefulness of the method.

References

[1]
Raja R, Dutta A, Venkatesh KS. New potential field method for rough terrain path planning using genetic algorithm for a 6-wheel rover. Robotics and Autonomous Systems. 2015 Oct 1;72:295--306.
[2]
Thomas G, Vantsevich VV. Wheel-terrain-obstacle interaction in vehicle mobility analysis. Vehicle system dynamics. 2010 Dec 1;48(S1):139--56.
[3]
Yang L, Qi J, Song D, Xiao J, Han J, Xia Y. Survey of robot 3D path planning algorithms. Journal of Control Science and Engineering. 2016 Mar 1;2016:5.
[4]
Muir P.F, Neuman C.P. Kinematic modeling of wheeled mobile robots. Journal of robotic systems. 1987 Apr;4(2):281--340.
[5]
Tarokh M, McDermott G, Hayati S, Hung J. Kinematic modeling of a high mobility Mars rover. InProceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C) 1999 (Vol. 2, pp. 992--998). IEEE.
[6]
Tarokh M, McDermott GJ. Kinematics modeling and analyses of articulated rovers. IEEE Transactions on Robotics. 2005 Aug;21(4):539--53.
[7]
Seegmiller N, Kelly A. Enhanced 3D Kinematic Modeling of Wheeled Mobile Robots. InRobotics: Science and Systems 2014 Jul (Vol. 2, No. 1, pp. 2--1).
[8]
Sutoh M, Otsuki M, Wakabayashi S, Hoshino T, Hashimoto T. The right path: comprehensive path planning for lunar exploration rovers. IEEE Robotics & Automation Magazine. 2015 Mar;22(1):22--33.
[9]
Zhu DJ, Latombe JC. New heuristic algorithms for efficient hierarchical path planning. IEEE Transactions on Robotics and Automation. 1991 Feb;7(1):9--20.
[10]
Garrido S, Moreno L, Blanco D, Jurewicz P. Path planning for mobile robot navigation using voronoi diagram and fast marching. Int. J. Robot. Autom. 2011 Jan 12;2(1):42--64.
[11]
Latombe JC. Motion planning: A journey of robots, molecules, digital actors, and other artifacts. The International Journal of Robotics Research. 1999 Nov;18(11):1119--28.
[12]
Guzmán JL, Berenguel M, Rodríguez F, Dormido S. An interactive tool for mobile robot motion planning. Robotics and Autonomous Systems. 2008 May 31;56(5):396--409.
[13]
Kavraki LE, Svestka P, Latombe JC, Overmars MH. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation. 1996; 12(4):566--580.
[14]
Finkel RA, Bentley JL. Quad trees a data structure for retrieval on composite keys. Acta informatica. 1974 Mar 1;4(1):1--9.
[15]
Cazenave T. Optimizations of data structures, heuristics and algorithms for path-finding on maps. In2006 IEEE symposium on computational intelligence and games 2006 May 22 (pp. 27--33). IEEE.
[16]
LaValle SM. Rapidly-exploring random trees: A new tool for path planning. Computer Science Dept., Iowa State University, Technical Report TR 98--11, 1998.
[17]
Zammit C, Van Kampen EJ. Comparison between A* and RRT algorithms for UAV path planning. In2018 AIAA Guidance, Navigation, and Control Conference 2018 (p. 1846).
[18]
Thrun S, Burgard W, Fox D. A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping. InICRA 2000 Apr 24 (Vol. 1, pp. 321--328).
[19]
Henry P, Krainin M, Herbst E, Ren X, Fox D. RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research. 2012 Apr;31(5):647--63.
[20]
Gutmann JS, Fukuchi M, Fujita M. 3D perception and environment map generation for humanoid robot navigation. The International Journal of Robotics Research. 2008 Oct;27(10):1117--34.
[21]
Noreen I, Khan A, Habib Z. A comparison of RRT, RRT* and RRT*-smart path planning algorithms. International Journal of Computer Science and Network Security (IJCSNS). 2016 Oct 1;16(10):20.
[22]
Takemura R, Ishigami G. Traversability-Based RRT* for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data. Journal of Robotics and Mechatronics. 2017 Oct 20;29(5):838--46.
[23]
Li M, Sun Q, Song Q, Wang Z, Li Y. Path Planning of Mobile Robot Based on RRT in Rugged Terrain. InProceedings of the 2nd International Conference on Computer Science and Application Engineering 2018 Oct 22 (p. 10). ACM.
[24]
Xin D, Hua-hua C, Wei-kang G. Neural network and genetic algorithm based global path planning in a static environment. Journal of Zhejiang University-Science A. 2005 Jun 1;6(6):549--54.
[25]
Seraji H, Howard A. Behavior-based robot navigation on challenging terrain: A fuzzy logic approach. IEEE Transactions on Robotics and Automation. 2002 Jun;18(3):308--21.
[26]
Raja R. "Rough Terrain Motion planning and redundancy resolution of an Articulated rover manipulator," PhD Thesis, IIT Kanpur, 2016.
[27]
Teka B, Raja R, Dutta A. Learning based end effector tracking control of a mobile manipulator for performing tasks on an uneven terrain. International Journal of Intelligent Robotics and Applications. 2019:1--3.
[28]
Aguilar WG, Morales SG. 3D environment mapping using the Kinect V2 and path planning based on RRT algorithms. Electronics. 2016 Oct 18;5(4):70.
[29]
Karaman S, Frazzoli E. Sampling-based algorithms for optimal motion planning. The international journal of robotics research. 2011 Jun;30(7):846--94.

Cited By

View all
  • (2023)End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space frameworkRobotica10.1017/S0263574723001479(1-37)Online publication date: 17-Nov-2023
  • (2022)Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-SpaceProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science10.1177/09544062211062435236:10(5663-5677)Online publication date: 19-Jan-2022
  • (2022)Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiringRobotica10.1017/S026357472100184340:8(2610-2629)Online publication date: 7-Jan-2022

Index Terms

  1. Path Planning and Obstacle Avoidance in CG Space of a 10 DOF Rover using RRT

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    AIR '19: Proceedings of the 2019 4th International Conference on Advances in Robotics
    July 2019
    423 pages
    ISBN:9781450366502
    DOI:10.1145/3352593
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • IITM: Indian Institute of Technology, Madras

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 January 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. CG space
    2. Path planning
    3. RRT* algorithm
    4. rover

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    AIR 2019
    AIR 2019: Advances in Robotics 2019
    July 2 - 6, 2019
    Chennai, India

    Acceptance Rates

    AIR '19 Paper Acceptance Rate 69 of 140 submissions, 49%;
    Overall Acceptance Rate 69 of 140 submissions, 49%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)12
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 06 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space frameworkRobotica10.1017/S0263574723001479(1-37)Online publication date: 17-Nov-2023
    • (2022)Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-SpaceProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science10.1177/09544062211062435236:10(5663-5677)Online publication date: 19-Jan-2022
    • (2022)Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiringRobotica10.1017/S026357472100184340:8(2610-2629)Online publication date: 7-Jan-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media