Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance
This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems fo...
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| Vydáno v: | ISA transactions Ročník 128; číslo Pt B; s. 664 - 676 |
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United States
Elsevier Ltd
01.09.2022
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| ISSN: | 0019-0578, 1879-2022, 1879-2022 |
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| Abstract | This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP.
•The trust-region filter is developed for SCP to construct low-complexity subproblems, which can enhance the computational efficiency.•The adaptive trust-region updating mechanism is designed to avoid infeasible iteration of TRF-SCP.•The effectiveness of TRF-SCP is verified on contrastive simulations and flight experiments. |
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| AbstractList | This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP. This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP. •The trust-region filter is developed for SCP to construct low-complexity subproblems, which can enhance the computational efficiency.•The adaptive trust-region updating mechanism is designed to avoid infeasible iteration of TRF-SCP.•The effectiveness of TRF-SCP is verified on contrastive simulations and flight experiments. This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP.This paper presents an trust-region filtered sequential convex programming (TRF-SCP) to reduce computational burdens of multi-UAV trajectory planning. In TRF-SCP, the trust-region based filter is proposed to remove the inactive collision-avoidance constraints of the convex programming subproblems for decreasing the complexity. The inactive constraints are detected based on the intersection relations between trust regions and collision-avoidance constraints. The trust-region based filter for different types of obstacles are tailored to address complex scenarios. An adaptive trust-region updating mechanism is also developed to mitigate infeasible iteration in TRF-SCP. The sizes of the trust regions are automatically adjusted according to the constraint violation of the optimized trajectory during the SCP iterations. TRF-SCP is then tested on several numerical multi-UAV formation scenarios involving cylindrical, spherical, conical, and polygon obstacles, respectively. Comparative studies demonstrate that TRF-SCP eliminates a large number of collision-avoidance constraints in the entire iterative process and outperforms SCP and Guaranteed Sequential Trajectory Optimization in terms of computational efficiency. The indoor flight experiments are presented to further evaluate the practicability of TRF-SCP. |
| Author | Xu, Guangtong Wang, Zhu Long, Teng Sun, Jingliang |
| Author_xml | – sequence: 1 givenname: Guangtong orcidid: 0000-0003-0683-5992 surname: Xu fullname: Xu, Guangtong email: guangtong_xu@163.com organization: Department of Precision Instrument, Tsinghua University, Beijing 100084, PR China – sequence: 2 givenname: Teng surname: Long fullname: Long, Teng email: tenglong@bit.edu.cn organization: School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, PR China – sequence: 3 givenname: Zhu orcidid: 0000-0003-3691-1407 surname: Wang fullname: Wang, Zhu email: wangzhubit@163.com organization: Department of Automation, North China Electric Power University, Baoding 071003, HeBei, PR China – sequence: 4 givenname: Jingliang surname: Sun fullname: Sun, Jingliang email: sunjingliangac@163.com organization: School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, PR China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34961607$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/IROS.2012.6385823 10.2514/1.39327 10.1007/s10514-012-9275-2 10.1109/LRA.2020.2964159 10.1109/ICRA.2015.7140034 10.1109/TCST.2010.2045501 10.3182/20140824-6-ZA-1003.02736 10.1109/TRO.2018.2857475 10.1109/CDC.2016.7798816 10.23919/ECC.2013.6669375 10.2514/1.G000218 10.1007/s11081-011-9176-9 10.2514/6.2018-3035 10.1109/ICRA.2019.8794205 10.2514/1.G002349 10.1007/s10957-015-0831-8 10.1016/j.isatra.2017.09.014 10.1016/j.automatica.2014.10.022 10.1109/LRA.2020.3047728 10.1177/0278364916632065 10.23919/ECC.2013.6669541 10.2514/1.62110 10.1109/LRA.2018.2890572 10.1016/j.automatica.2010.10.037 10.1007/s42064-017-0003-8 10.1109/TAES.2018.2890375 |
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| Keywords | Adaptive trust-region updating Multi-UAV trajectory planning Sequential convex programming Trust-region based filter Inactive constraints |
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| SubjectTerms | Adaptive trust-region updating Inactive constraints Multi-UAV trajectory planning Sequential convex programming Trust-region based filter |
| Title | Trust-region filtered sequential convex programming for multi-UAV trajectory planning and collision avoidance |
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