Fleet Management System for an Industry Environment
DOI:
https://doi.org/10.18196/jrc.v3i6.16298Keywords:
Fleet management, ROS, Path, Mobile robotAbstract
The article deals with the management of a fleet of AMR robots that perform logistics in production. The entire system design is implemented in the ROS environment - state of the art for the development in robotics. Four already available solutions for fleet management in ROSe are analyzed in detail in the article. These solutions fail when there is a need to change the route plan in a dynamically changing environment. Likewise, some did not sufficiently synchronize the movement of the robots and collisions occurred or, with a larger number of robots, represented an enormous computational load. Our solution was designed to be as simple and reliable as possible for industrial use. It is based on a combination of semi-autonomous and centralized approach. A hybrid map is used for planning the movement of the robot fleet, which provides the advantages of both a metric and a topological map. This route map for a fleet of robots can be easily drawn in readily available CAD software. Synchronization of robots was designed on the principle of semaphore or mutex, which enabled the use of bidirectional paths. The results are verified in simulations and were aimed at verifying the proposed robot synchronization. It was confirmed that the proposed synchronization slows down the robots, but there were no collision situations. By separating route planning from synchronization, we simplified the entire fleet management process and thus created a very efficient system for network and hardware resources. In addition, the system is easily expandable.References
B. Binder, “Spatio-temporal prioritized planning,” in Ph.D. Thesis, Wien, 2017.
D. Claes et al., “Collision avoidance under bounded localization uncertainty,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1192-1198, 2012.
W. Guan, S. Chen, S. Wen, Z. Tan, H. Song and W. Hou, "High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning," in IEEE Photonics Journal, vol. 12, no. 2, pp. 1-16, April 2020, Art no. 7901716, doi: 10.1109/JPHOT.2020.2981485.
X. Li et al., “A review of industrial wireless networks in the context of industry 4.0,” in Wireless networks, pp. 23-41, 2017.
Library with search algorithms for task and path planning for multi robot/agent systems. [online]. Available on: https://github.com/whoenig/libMultiRobotPlanning.
A. Mannucci et al., “Provably safe multi-robot coordination with unreliable communication,” in IEEE Robotics and Automation Letters, pp. 3232-3239, 2019.
F. Pecora et al., “A Loosely-Coupled Approach for Multi-Robot Coordination, Motion Planning and Control,” in ICAPS, pp. 485-493, 2018.
V. Vavrík et al., “The design of manufacturing line configurations with multi-agent logistics system,” Transportation Research Procedia, pp. 1224-1230, 2019.
Hajduk et al., “Developing new behavior strategies of robot soccer team SjF TUKE Robotics: Category MiroSot,” in International Journal of Advanced Robotic Systems, 2016, doi: 10.1177/1729881416663670.
M. C., Lucas-Estan, “Emerging trends in hybrid wireless communication and data management for the industry 4.0,” in Electronics, 2018, doi: 10.3390/electronics7120400.
R. Jánoš and M. Sukop, “Modular robots on multiagent principe,” in Глобальне управління та економіка, pp. 57-60, 2015.
A. Janota et al., “Functional Behavior of Traffic Control Systems Learned Through Multiagent Systems,” in International Workshop on the Educational Uses of Multi-Agent Systems (EduMAS), 2009.
J. Adam, “What is the V-model approach to software development and testing?” [online]. Available on: https://kruschecompany.com/v-model-software-development-methodology/
E. A. Oyekanlu et al., “A review of recent advances in automated guided vehicle technologies: Integration challenges and research areas for 5G-based smart manufacturing applications,” in IEEE access, pp. 202312-202353, 2020, doi: 10.1109/ACCESS.2020.3035729.
G. Fragapane et al., “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” in European Journal of Operational Research, pp. 405-426, 2021, doi: 10.1016/j.ejor.2021.01.019.
A. Liaqat et al., “Autonomous mobile robots in manufacturing: Highway Code development, simulation, and testing,” in The International Journal of Advanced Manufacturing Technology, pp. 4617-4628, 2019, doi: 10.1007/s00170-019-04257-1
A. Singhal et al., “Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform,” in European Conference on Mobile Robots (ECMR), pp. 1-6, 2017, doi: 10.1109/ECMR.2017.8098721.
B. Kuipers et al., “Local metrical and global topological maps in the hybrid spatial semantic hierarchy,” in IEEE International Conference on Robotics and Automation, pp. 4845-4851, 2004, doi: 10.1109/ROBOT.2004.1302485.
P. Neher et al., “Identification and Classification of the Communication Data of Automated Guided Vehicles and Autonomous Mobile Robots,” in 8th International Conference on Automation, Robotics and Applications, pp. 68-75, 2022, doi: 10.1109/ICARA55094.2022.9738572.
B. Binder et al., “Multi robot route planning (MRRP): Extended spatial-temporal prioritized planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4133-4139, 2019, doi: 10.1109/IROS40897.2019.8968465.
H. Dong et al., “The path planning for mobile robot based on Voronoi diagram,” in Third International Conference on Intelligent Networks and Intelligent Systems, pp. 446-449, 2010, doi: 10.1109/ICINIS.2010.105.
D. Hennes et al., “Multi-robot collision avoidance with localization uncertainty,” in AAMAS, pp. 147-154, 2012.
H. Ma et al., “Overview: A hierarchical framework for plan generation and execution in multirobot systems,” in IEEE Intelligent Systems, pp. 6-12, 2017, doi: 10.1109/MIS.2017.4531217.
F. Pecora et al., “A loosely-coupled approach for multi-robot coordination, motion planning and control,” in Twenty-eighth international conference on automated planning and scheduling, 2018.
J. Conesa-Muñoz et al., “Distributed multi-level supervision to effectively monitor the operations of a fleet of autonomous vehicles in agricultural tasks,” in Sensors, pp. 5402-5428, 2015, doi: 10.3390/s150305402.
P. Forte et al., “Online task assignment and coordination in multi-robot fleets,” in IEEE Robotics and Automation Letters, pp. 4584-4591, 2021, doi: 10.1109/LRA.2021.3068918.
M. Berndt et al., “Centralized Robotic Fleet Coordination and Control,” in Mobile Communication-Technologies and Applications; 25th ITG-Symposium, pp. 1-8, 2021.
A. Renawi et al., “ROS validation for non-holonomic differential robot modeling and control: Case study: Kobuki robot trajectory tracking controller,” in 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), pp. 1-5, 2017, doi: 10.1109/ICMSAO.2017.7934880.
N. Gallardo et al., “Formation control implementation using kobuki turtlebots and parrot bebop drone,” in World Automation Congress (WAC), pp. 1-6, 2016, doi: 10.1109/WAC.2016.7582996.
F. Duchoň et al., “Path planning with modified a star algorithm for a mobile robot,” in Procedia Engineering, pp. 59-69, 2014, doi: 10.1016/j.proeng.2014.12.098.
C. Iordache et al., “Smart Pointers and Shared Memory Synchronisation for Efficient Inter-process Communication in ROS on an Autonomous Vehicle,” inIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6441-6448, 2021, doi: 10.1109/IROS51168.2021.9636018.
B. Dieber et al., “Penetration testing ROS,” in Robot operating system (ROS), pp. 183-225, 2020, doi: 10.1007/978-3-030-20190-6_8.
S. Adam and U.P. Schultz, “Towards interactive, incremental programming of ros nodes,” in arXiv preprint arXiv:1412.4714, 2014.
M. Quigley et al., “ROS: an open-source Robot Operating System,” in ICRA workshop on open source software, pp. 1-6, 2009.
Y. Abdelrasoul et al., “A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM,” in 2nd IEEE international symposium on robotics and manufacturing automation (ROMA), pp. 1-6, 2016, doi: 10.1109/ROMA.2016.7847825.
R. K. Megalingam et al., “ROS based autonomous indoor navigation simulation using SLAM algorithm,” in International Journal of Pure and Applied Mathematics, pp. 199-205, 2018.
W. P. N. D. Reis et al., “An extended analysis on tuning the parameters of Adaptive Monte Carlo Localization ROS package in an automated guided vehicle,” in The International Journal of Advanced Manufacturing Technology, pp. 1975-1995, 2021, doi: 0.1007/s00170-021-07437-0.
W. P. N. D. Reis et al., “A quantitative study of tuning ros adaptive monte carlo localization parameters and their effect on an agv localization,” in 19th International Conference on Advanced Robotics (ICAR), pp. 302-307, 2019, doi: 10.1109/ICAR46387.2019.8981601.
I. Hassani et al., “Turning Point and Free Segments Strategies for Navigation of Wheeled Mobile Robot,” in International Journal of Robotics and Control Systems, pp. 172-186, 2022, doi: 10.31763/ijrcs.v2i1.586.
X. Wei-hong et al., “Review of Aerial Manipulator and its Control,” in International Journal of Robotics and Control Systems, pp. 308-325, 2021, doi: 10.31763/ijrcs.v1i3.363.
P. Raja and S. Pugazhenthi, “Optimal path planning of mobile robots: A review,” in international journal of physical sciences, pp. 1314-1320, 2012, doi: 10.5897/IJPS11.1745.
M. N. Zafar and J. C. Mohanta, “Methodology for path planning and optimization of mobile robots: A review,” in Procedia computer science, pp. 141-152, 2018, doi: 10.1016/j.procs.2018.07.01.
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