Model predictive trajectory tracking control of automated guided vehicle in complex environments
Autonomous navigation in a real-world industrial environment is in many ways a challenging task. One of the key challenges is rapid collision-free planning and execution of trajectories to reach any target position and orientation with high accuracy, taking into account the limitations of imperfectn...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
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2020
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| Online Access: | https://hdl.handle.net/10356/143716 |
| _version_ | 1811689351445217280 |
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| author | Chen, Chun-Lin Li, Juncheng Li, Maoxun Xie, Lihua |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chen, Chun-Lin Li, Juncheng Li, Maoxun Xie, Lihua |
| author_sort | Chen, Chun-Lin |
| collection | NTU |
| description | Autonomous navigation in a real-world industrial environment is in many ways a challenging task. One of the key challenges is rapid collision-free planning and execution of trajectories to reach any target position and orientation with high accuracy, taking into account the limitations of imperfectness of the vehicle. The model prediction-based motion planners have been successfully used in recent years to generate feasible motions for imperfect vehicles. This paper develops and implements a Model Predictive Control (MPC)-based trajectory controller for path tracking problem in narrow corridors. To evaluate the performance of the proposed method, we designed comparative simulations and experiments. We confirm that the proposed MPC-based controller can track the trajectory precisely and smoothly in specific complex environments. In addition, the proposed methodology can also be a suitable solution to other way-point tracking situations for an industrial mobile robot. |
| first_indexed | 2024-10-01T05:46:43Z |
| format | Conference Paper |
| id | ntu-10356/143716 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:46:43Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1437162020-09-18T05:55:45Z Model predictive trajectory tracking control of automated guided vehicle in complex environments Chen, Chun-Lin Li, Juncheng Li, Maoxun Xie, Lihua School of Electrical and Electronic Engineering 2018 IEEE 14th International Conference on Control and Automation (ICCA) Delta-NTU Corporate Laboratory Engineering::Electrical and electronic engineering Automated Guided Vehicles Robotics Autonomous navigation in a real-world industrial environment is in many ways a challenging task. One of the key challenges is rapid collision-free planning and execution of trajectories to reach any target position and orientation with high accuracy, taking into account the limitations of imperfectness of the vehicle. The model prediction-based motion planners have been successfully used in recent years to generate feasible motions for imperfect vehicles. This paper develops and implements a Model Predictive Control (MPC)-based trajectory controller for path tracking problem in narrow corridors. To evaluate the performance of the proposed method, we designed comparative simulations and experiments. We confirm that the proposed MPC-based controller can track the trajectory precisely and smoothly in specific complex environments. In addition, the proposed methodology can also be a suitable solution to other way-point tracking situations for an industrial mobile robot. Accepted version The research is partially supported by the Delta - NTU Corporate Lab through the NRF corporate lab@university scheme. The authors would like to thank Mr. Yongjun Wee and Dr. Jeffrey Soon for providing us use cases and many discussions during the course of this work. 2020-09-18T05:31:04Z 2020-09-18T05:31:04Z 2018 Conference Paper Chen, C.-L., Li, J., Li, M., & Xie, L. (2018). Model predictive trajectory tracking control of automated guided vehicle in complex environments. 2018 IEEE 14th International Conference on Control and Automation (ICCA), 405-410. doi:10.1109/ICCA.2018.8444247 9781538660898 https://hdl.handle.net/10356/143716 10.1109/ICCA.2018.8444247 2-s2.0-85053116705 405 410 en SMA-RP3 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at https://doi.org/10.1109/ICCA.2018.8444247 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Automated Guided Vehicles Robotics Chen, Chun-Lin Li, Juncheng Li, Maoxun Xie, Lihua Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title | Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title_full | Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title_fullStr | Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title_full_unstemmed | Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title_short | Model predictive trajectory tracking control of automated guided vehicle in complex environments |
| title_sort | model predictive trajectory tracking control of automated guided vehicle in complex environments |
| topic | Engineering::Electrical and electronic engineering Automated Guided Vehicles Robotics |
| url | https://hdl.handle.net/10356/143716 |
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