Abstract
This paper proposes an online decentralized and asynchronous multiagent trajectory planning algorithm in dense environments. In our algorithm, the optimization problem is transformed into a quadratic programming (QP) problem to reduce the computational complexity by constructing the optimal linear flight corridors (OLFC). A cooperation-based deconfliction framework is designed to ensure the safety and feasibility under the decentralized and asynchronous architecture. We conduct a large number of simulations to verify the reliability and efficiency of our algorithm in dense environments with higher success rate, less computational time and total navigation time, which is more aggressive and cooperative.
This work is funded by National Natural Science Foundation of China (61803309), Key Research and Development Project of Shaanxi Province (2020ZDLGY06-02, 2021ZDLGY07-03), Aeronautical Science Foundation of China (2019ZA053008, 20185553034), CETC Key Laboratory of Data Link Technology Open Project Fund (\(\mathrm {CLDL-20202101_{-}2}\)).
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Guo, Z., Hu, J., Zhao, C., Pan, Q. (2023). Asynchronous and Decentralized Multiagent Trajectory Planning in Dense Environments. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_15
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