Development of navigation system for autonomous guided vehicle localization with measurement uncertainties
Author affiliations
DOI:
https://doi.org/10.15625/2525-2518/16274Keywords:
Autonomous Ground Vehicles, navigation system, visibility graph, Dijkstra's algorithm, triangular decompositionAbstract
Recently, Autonomous Ground Vehicles (AGV) have been dramatically developed in various engineering applications, such as Industry 4.0 manufacturing and smart technology. Mapping navigation plays a critical role in the movement of the AGV in a cluttered environment. Hence, several problems related to this field must be addressed for a wide application of AGV in reality. In this paper, an innovative methodology is proposed for AGV localization with measurement uncertainties. Overall, this approach has a total of three key steps. To begin with, a path planning is designed to establish a safe, effective, and optimal path. Particularly, the visibility graph is built by determining a geometric free configuration space of the AGV. The Dijkstra's algorithm is applied to the visibility graph to find a feasible path which has random starting and ending points. After that, the enhanced triangular decomposition method is utilized to quickly localize the AGV in two-dimensional space. Finally, the navigation system is developed to optimize the pathways for the continuous movement of the AGVs. Extensive experiments are conducted among different scenarios to evaluate the precision and stability of the proposed method.Downloads
References
Guo K., Zhu J., and Shen L. - An Improved Acceleration Method Based on Multi-Agent System for AGVs Conflict-Free Path Planning in Automated Terminals, IEEE Access 9 (2021) 3326-3338, doi: 10.1109/ACCESS.2020.3047916.
Lu S., Xu C., and Zhong R. Y. - An Active RFID Tag-Enabled Locating Approach With Multipath Effect Elimination in AGV, IEEE Transactions on Automation Science and Engineering 13 (3) (2016) 1333-1342, doi: 10.1109/TASE.2016.2573595.
Digani V., Sabattini L., Secchi C., and Fantuzzi C. - Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses, IEEE Transactions on Automation Science and Engineering 12 (3) (2015) 922-934.
doi: 10.1109/TASE.2015.2446614.
Nguyen A. T., Nguyen V. T., Nguyen X. T., Vu C. T. - Development of a Multiple-Sensor Navigation System for Autonomous Guided Vehicle Localization, in: Tran D. T., Jeon G., Nguyen T. D. L., Lu J., Xuan T. D. (Eds.) Intelligent Systems and Networks. ICISN 2021. Lecture Notes in Networks and Systems, Vol. 243, 2021. https://doi.org/10.1007/978-981-16-2094-2_49.
Digani V., Hsieh M. A., Sabattini L. et al. - Coordination of multiple AGVs: a quadratic optimization method, Auton Robot 43 (2019) 539-555. https://doi.org/10.1007/s10514-018-9730-9
Orozco-Rosas U., Picos K., and Montiel O. - Hybrid Path Planning Algorithm Based on Membrane Pseudo-Bacterial Potential Field for Autonomous Mobile Robots, IEEE Access 7 (2019) 156787-156803. doi: 10.1109/ACCESS.2019.2949835.
Mac T. T., Copot C., Duc T. T., and De Keyser R. - A hierarchical global path planning based on multi-objective particle swarm optimization, 21st International Conference on Methods and Models in Automation and Robotics (MMAR), 2016, pp. 930-935, doi: 10.1109/MMAR.2016.7575262.
Gu Z., Yin T., and Ding Z. - Path Tracking Control of Autonomous Vehicles Subject to Deception Attacks via a Learning-Based Event-Triggered Mechanism, IEEE Transactions on Neural Networks and Learning Systems, 2021. doi: 10.1109/TNNLS.2021.3056764.
Nguyen V. T., Lin C., Su S., and Tran Q. - Adaptive PID tracking control based radial basic function networks for a 2-DOF parallel manipulator, International Conference on System Science and Engineering (ICSSE), 2017, pp. 309-312.
doi: 10.1109/ICSSE.2017.8030887.
Nguyen V. T., Lin C., Su S., and Tran Q. - Adaptive PD networks tracking control with full-state constraints for redundant parallel manipulators, 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), 2017, pp. 1-5, 2017.
doi: 10.1109/IFSA-SCIS.2017.8023289.
Nguyen V. T., Su S. F., Wang N., and Sun W. - Adaptive finite-time neural network control for redundant parallel manipulators, Asian Journal of Control 22 (6) (2020) 2534-2542. https://doi.org/10.1002/asjc.2120.
Yuan J., Zhang S., Sun Q., Liu G., and Cai J. - Laser-Based Intersection-Aware Human Following With a Mobile Robot in Indoor Environments, IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (1) (2021) 354-369.
doi: 10.1109/TSMC.2018.2871104.
Wang X., Shi H., and Zhang C. - Path Planning for Intelligent Parking System Based on Improved Ant Colony Optimization, IEEE Access, Vol. 8, pp. 65267-65273, 2020, doi: 10.1109/ACCESS.2020.2984802.
Nguyen V. T., Nguyen A. T., Nguyen V. T., Bui H. A. - A Real-Time Human Tracking System Using Convolutional Neural Network and Particle Filter, In: Tran D. T., Jeon G., Nguyen T. D. L., Lu J., Xuan T. D. (Eds.) Intelligent Systems and Networks. ICISN 2021. Lecture Notes in Networks and Systems, Vol. 243. Springer, Singapore, 2021. https://doi.org/10.1007/978-981-16-2094-2_50.
Nguyen V. T., Nguyen A. T., Nguyen V. T., Nguyen X. T., Bui H. A. - Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm, Advances in Science, Technology and Engineering Systems Journal 6 (2) (2021) 907-914. Doi: 10.25046/aj0602103.
Ali H., Gong D., Wang M., and Dai X. - Path planning of mobile robot with improved ant colony algorithm and MDP to produce smooth trajectory in grid-based environment, Frontiers Neurorobotics 14 (2020) 15. doi: 10.3389/fnbot.2020.00044.
Li Y., Ming Y., Zhang Z., Yan W., and Wang K. - An Adaptive Ant Colony Algorithm for Autonomous Vehicles Global Path Planning, IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021, pp. 1117-1122, doi: 10.1109/CSCWD49262.2021.9437682.
Zhao H., Nie Z., Zhou F., and Lu S. - A Compound Path Planning Algorithm for Mobile Robots, IEEE International Conference on Power Electronics, Computer Applications (ICPECA), 2021, pp. 1-5, doi: 10.1109/ICPECA51329.2021.9362724.
Zeyu C., Zixiao F., and Zhiqiang F. - Improved AGV Path Planning Algorithm Based on Grid Map Model, IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021, pp. 2632-2635, doi: 10.1109/ IAEAC50856.2021.9390767.
Wang X., Shi H., and Zhang C. - Path Planning for Intelligent Parking System Based on Improved Ant Colony Optimization, IEEE Access, Vol. 8, pp. 65267-65273, 2020, doi: 10.1109/ACCESS.2020.2984802.
Liu Y., Hou Z., Tan Y., Liu H., and Song C. - Research on Multi-AGVs Path Planning and Coordination Mechanism, IEEE Access, Vol. 8, pp. 213345-213356, 2020, doi: 10.1109/ACCESS.2020.3039959.
Tang G., Tang C., Claramunt C., Hu X., and Zhou P. - Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment, IEEE Access, Vol. 9, pp. 59196-59210, 2021, doi: 10.1109/ACCESS.2021.3070054.
Nishi T. and Maeno R. - Petri Net Decomposition Approach to Optimization of Route Planning Problems for AGV Systems, IEEE Transactions on Automation Science and Engineering 7 (3) (2010) 523-537. doi: 10.1109/TASE.2010.2043096.
S. Reveliotis, ‘‘An MPC scheme for traffic coordination in open and irreversible, zone-controlled, guidepath-based transport systems,’’ IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1528–1542, Jul. 2020.
Zhang S. J., Wang X., and Huang K. - On-line scheduling of order picking and delivery with multiple zones and limited vehicle capacity,’ Omega, Vol. 79, 2018, pp. 104-115. https://doi.org/10.1016/j.omega.2017.08.004.
Rozsa Z. and Sziranyi T. - Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor, IEEE Transactions on Intelligent Transportation Systems 19 (8) (2018) 2708-2720. doi: 10.1109/TITS.2018.2790264.
Luo M., Hou X., and Yang J. - Surface Optimal Path Planning Using an Extended Dijkstra Algorithm, IEEE Access, Vol. 8, 2020, pp. 147827-147838.
doi: 10.1109/ACCESS.2020.3015976.
D. -D. Zhu and J. -Q. Sun, "A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute," IEEE Access, vol. 9, pp. 19761-19775, 2021, doi: 10.1109/ACCESS.2021.3053169.
Zhang Z., Guo Q., Chen J. and Yuan P. - Collision-Free Route Planning for Multiple AGVs in an Automated Warehouse Based on Collision Classification, IEEE Access, Vol. 6, pp. 26022-26035, 2018, doi: 10.1109/ACCESS.2018.2819199.
Marcelo Kallmann, Mubbasir Kapadia - Geometric and Discrete Path Planning for Interactive Virtual Worlds, Morgan & Claypool, 2016.
Cristian Mahulea, Marius Kloetzer, Ramon Gonzalez - Cell Decomposition Algorithms, in Path Planning of Cooperative Mobile Robots Using Discrete Event Models, IEEE, 2020, pp. 41-70, doi: 10.1002/9781119486305.ch3.
Giordano A. M., Ott C., and Albu-Schäffer A. - Coordinated Control of Spacecraft's Attitude and End-Effector for Space Robots," IEEE Robotics and Automation Letters 4 (2) (2019) 2108-2115. doi: 10.1109/LRA.2019.2899433.
Wei X., et al. - Image Redundancy Filtering for Panorama Stitching, IEEE Access, Vol. 8, pp. 209113-209126, 2020, doi: 10.1109/ACCESS.2020.3038178.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Vietnam Journal of Sciences and Technology (VJST) is an open access and peer-reviewed journal. All academic publications could be made free to read and downloaded for everyone. In addition, articles are published under term of the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) Licence which permits use, distribution and reproduction in any medium, provided the original work is properly cited & ShareAlike terms followed.
Copyright on any research article published in VJST is retained by the respective author(s), without restrictions. Authors grant VAST Journals System a license to publish the article and identify itself as the original publisher. Upon author(s) by giving permission to VJST either via VJST journal portal or other channel to publish their research work in VJST agrees to all the terms and conditions of https://creativecommons.org/licenses/by-sa/4.0/ License and terms & condition set by VJST.
Authors have the responsibility of to secure all necessary copyright permissions for the use of 3rd-party materials in their manuscript.