Development of an omnidirectional AGV by applying ORB-SLAM for navigation under ROS framework

• Autorzy: Wu Pan-Long , Li Jyun-Jhen , Shaw Jin-Siang

Identyfikatory

DOI

10.14313/JAMRIS/1-2022/2

• Języki publikacji: EN

Abstrakty

EN

This paper presents the development of an automated guided vehicle with omni-wheels for autonomous navigation under a robot operating system framework. Specifically, a laser rangefinder-constructed two-dimensional environment map is integrated with a three-dimensional point cloud map to achieve real-time robot positioning, using the oriented features from accelerated segment testing and a rotated binary robust independent elementary feature detector-simultaneous localization and mapping algorithm. In the path planning for autonomous navigation of the omnidirectional mobile robot, we applied the A* global path search algorithm, which uses a heuristic function to estimate the robot position difference and searches for the best direction. Moreover, we employed the time-elastic-band method for local path planning, which merges the time interval of two locations to realize time optimization for dynamic obstacle avoidance. The experimental results verified the effectiveness of the applied algorithms for the omni-wheeled mobile robot. Furthermore, the results showed a superior performance over the adaptive Monte Carlo localization for robot localization and dynamic window approach for local path planning.

Słowa kluczowe

EN

ROS; ORB-SLAM; omnidirectional AGV; autonomous Navigation

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 1
• Strony: 14--20
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Wu Pan-Long

• Nanjing University of Science and Technology, China

autor

Li Jyun-Jhen

• National Taipei University of Technology, Taiwan

autor

Shaw Jin-Siang

• National Taipei University of Technology, Taiwan

Bibliografia

• [1] R. Mur-Artal, J.M.M. Montiel and J.D. Tardós, “ORB-SLAM: A versatile and accurate monocular SLAM system”, IEEE Trans Robot, vol. 31, no. 5, 2015, pp. 1147–1163.
• [2] R. Mur-Artal and J.D. Tardos, “ORB-SLAM2: An open-source SLAM system for monocular, stereo and RGB-D cameras”, IEEE Trans Robot, vol. 33, no. 5, 2017, pp. 1255–1262.
• [3] G. Grisetti, C. Stachniss and W. Burgard, “Improved techniques for grid mapping with Rao-Blackwellized particle filters”, IEEE Trans Robot, vol. 23, no. 1, 2007, pp. 34–46.
• [4] A.J. Bostel and V.K. Sagar, “Dynamic control systems for AGVs”, Comput Control Eng J, vol. 7, no. 4, 1996, pp. 169–176.
• [5] D. Fox, W. Burgard and S. Thrun, “The dynamic window approach to collision avoidance”, IEEE Robot Autom Mag, vol. 4, no. 1, 1997, pp. 23–33.
• [6] C. Rösmann, W. Feiten, T. Wösch, et al., “Efficient trajectory optimization using a sparse model”, European Conference on Mobile Robots, Barcelona, Spain, 25–27 September 2013, pp. 138-143.
• [7] K.V. Ignatiev, M.M. Kopichev and A.V. Putov, “Autonomous omni-wheeled mobile robots”, 2nd International Conference on Industrial Engineering, Applications and Manufacturing, Chelyabinsk, Russia, 19–20 May 2016, pp. 1-4.
• [8] S.A. Magalhães, A.P. Moreira and P. Costa, “Omnidirectional robot modeling and simulation”, IEEE International Conference on Autonomous Robot Systems and Competitions, Ponta Delgada, Portugal, 15–16 April 2020, pp. 251-256.
• [9] J. Xin, X.L. Jiao, Y. Yang, et al., “Visual navigation for mobile robot with Kinect camera in dynamic environment”, 35th Chinese Control Conference, Chengdu, China, 27–29 July 2016, pp. 4757-4764.
• [10] Z. Meng, C. Wang, Z. Han, et al., “Research on SLAM navigation of wheeled mobile robot based on ROS”, 5th International Conference on Automation, Control and Robotics Engineering, Dalian, China, 19–20 September 2020, pp. 110-116.
• [11] Y. Feng, C. Ding, X. Li, et al., “Integrating Mecanum wheeled omni-directional mobile robots in ROS”, IEEE International Conference on Robotics and Biomimetics, Qingdao, China, 3–7 December 2016, pp. 643-648.
• [12] B.A. Berg, Markov chain Monte Carlo simulations and their statistical analysis. Hackensack, New Jersey: World Scientific, 2004.
• [13] R. Akkaya and F.A. Kazan, “A new method for angular speed measurement with absolute encoder”, Elektron Elektrotech, vol. 26, no. 1, 2020, pp. 18–22.
• [14] Figure source: Open-Base. https://github.com/GuiRitter/OpenBase
• [15] J. Goncalves, J. Lima and P. Costa, “Real time tracking of an omnidirectional robot – An extended Kalman filter approach”, 5th International Conference on Informatics in Control, Automation and Robotics, Funchal, Portugal, 11–15 May 2008, pp. 5–10.
• [16] Package source: move_base. http://wiki.ros.org/move_base
• [17] J.G. Ziegler and N.B. Nichols, “Optimum settings for automatic controllers”, J Dyn Sys Meas Control, vol. 115, no. 2B, 1993, pp. 220–222.
• [18] E.B. Olson, “Real-time correlative scan matching”, IEEE International Conference on Robotics and Automation, Kobe, Japan, 12–17 May 009, pp. 4387-4393.
• [19] M. Seder and I. Petrovic, “Dynamic window based approach to mobile robot motion control in the presence of moving obstacles”, IEEE International Conference on Robotics and Automation, Rome, Italy, 10–14 April 2007, pp. 1986-1991.
• [20] C. Rösmann, F. Hoffmann and T. Bertram, “Planning of multiple robot trajectories in distinctive topologies”, European Conference on Mobile Robots, Lincoln, UK, 2–4 September 2015, pp. 1-6.
• [21] P.L. Wu, Z.M. Zhang, C.J. Liew, et al., “Hybrid navigation of an autonomous mobile robot to depress an elevator button”, Journal of Automation, Mobile Robotics and Intelligent Systems, accepted, 2022.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).