MPC Based Vehicular Trajectory Planning in Structured Environment
In this paper, the hierarchical architecture of trajectory planning and control is set up for safe driving with multiple participants without collision, where both levels utilize a time-varying model predictive control methodology. Firstly, a high-level planner formulates an optimal control problem...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9328410/ |
| _version_ | 1818879945005334528 |
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| author | Qing Shi Jin Zhao Abdelkader El Kamel Ismael Lopez-Juarez |
| author_facet | Qing Shi Jin Zhao Abdelkader El Kamel Ismael Lopez-Juarez |
| author_sort | Qing Shi |
| collection | DOAJ |
| description | In this paper, the hierarchical architecture of trajectory planning and control is set up for safe driving with multiple participants without collision, where both levels utilize a time-varying model predictive control methodology. Firstly, a high-level planner formulates an optimal control problem to obtain an optimal trajectory while satisfying different constraints. In particular, due to obstacles' occupation, several partition functions are generated as linear collision constraints through an optimization process in order to convexify the collision-free region into sub-regions. Secondly, the low-level controller receives the desired trajectory from the high-level planner, and then computes an appropriate steering angle to execute the planned maneuver. Both levels are formulated within the model predictive control(MPC) methodology. The strength of this framework is that it combines different constraints in each optimal control problem. Including a high-level planner ensures the feasibility of safe trajectory planning and the use of a low-level controller ensures tracking stability for safe driving, even under various collision constraints and model mismatch between system plant and predictive process model. Finally, several simulations verified the proposed framework, which was used to compute an optimal, safe trajectory over a set of static or moving obstacles and stabilize the vehicle around it. |
| first_indexed | 2024-12-19T14:38:08Z |
| format | Article |
| id | doaj.art-08e87d473ea24942bb8c5a9825de63a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:38:08Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-08e87d473ea24942bb8c5a9825de63a42022-12-21T20:17:11ZengIEEEIEEE Access2169-35362021-01-019219982201310.1109/ACCESS.2021.30527209328410MPC Based Vehicular Trajectory Planning in Structured EnvironmentQing Shi0https://orcid.org/0000-0003-3802-6938Jin Zhao1Abdelkader El Kamel2Ismael Lopez-Juarez3https://orcid.org/0000-0001-6405-5519CRIStAL, Centrale Lille Institut, Villeneuve-d’Ascq, FranceKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guizhou, ChinaCRIStAL, Centrale Lille Institut, Villeneuve-d’Ascq, FranceCenter for Research and Advanced Studies (CINVESTAV), Ramos Arizpe, MexicoIn this paper, the hierarchical architecture of trajectory planning and control is set up for safe driving with multiple participants without collision, where both levels utilize a time-varying model predictive control methodology. Firstly, a high-level planner formulates an optimal control problem to obtain an optimal trajectory while satisfying different constraints. In particular, due to obstacles' occupation, several partition functions are generated as linear collision constraints through an optimization process in order to convexify the collision-free region into sub-regions. Secondly, the low-level controller receives the desired trajectory from the high-level planner, and then computes an appropriate steering angle to execute the planned maneuver. Both levels are formulated within the model predictive control(MPC) methodology. The strength of this framework is that it combines different constraints in each optimal control problem. Including a high-level planner ensures the feasibility of safe trajectory planning and the use of a low-level controller ensures tracking stability for safe driving, even under various collision constraints and model mismatch between system plant and predictive process model. Finally, several simulations verified the proposed framework, which was used to compute an optimal, safe trajectory over a set of static or moving obstacles and stabilize the vehicle around it.https://ieeexplore.ieee.org/document/9328410/Trajectory planningcollision avoidancemodel predictive control |
| spellingShingle | Qing Shi Jin Zhao Abdelkader El Kamel Ismael Lopez-Juarez MPC Based Vehicular Trajectory Planning in Structured Environment IEEE Access Trajectory planning collision avoidance model predictive control |
| title | MPC Based Vehicular Trajectory Planning in Structured Environment |
| title_full | MPC Based Vehicular Trajectory Planning in Structured Environment |
| title_fullStr | MPC Based Vehicular Trajectory Planning in Structured Environment |
| title_full_unstemmed | MPC Based Vehicular Trajectory Planning in Structured Environment |
| title_short | MPC Based Vehicular Trajectory Planning in Structured Environment |
| title_sort | mpc based vehicular trajectory planning in structured environment |
| topic | Trajectory planning collision avoidance model predictive control |
| url | https://ieeexplore.ieee.org/document/9328410/ |
| work_keys_str_mv | AT qingshi mpcbasedvehiculartrajectoryplanninginstructuredenvironment AT jinzhao mpcbasedvehiculartrajectoryplanninginstructuredenvironment AT abdelkaderelkamel mpcbasedvehiculartrajectoryplanninginstructuredenvironment AT ismaellopezjuarez mpcbasedvehiculartrajectoryplanninginstructuredenvironment |