Online Whole-Body Motion Planning for Quadrotor using Multi-Resolution Search

Ren, Yunfan; Liang, Siqi; Zhu, Fangcheng; Lu, Guozheng; Zhang, Fu

doi:10.1109/icra48891.2023.10160767

Cited by 6 publications

(1 citation statement)

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“…While the planning and control of multicopter UAVs have been comprehensively studied by leveraging the differential flatness property of the systems (Mellinger and Kumar, 2011; Faessler et al, 2018), thus stimulating a wealth of practical applications, like flying through narrow gaps (Mellinger et al, 2012; Falanga et al, 2017; Ren et al, 2023), perching on structures (Mellinger et al, 2012; Hang et al, 2019), autonomous safe navigation (Shen et al, 2011; Zhou et al, 2019; Zhang et al, 2020), and drone racing (Foehn et al, 2021), the equivalent techniques for tail-sitter UAVs are relatively underdeveloped. The differential flatness for tail-sitters, which resolves the full states and inputs of the system from finite flat outputs and their derivatives, has not been rigorously investigated.…”

Section: Introductionmentioning

confidence: 99%

Trajectory generation and tracking control for aggressive tail-sitter flights

Lu,

Cai,

Chen

et al. 2023

The International Journal of Robotics Research

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We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate (not simplified) aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints, and actuator saturation. The planned trajectory of flat output is transformed into state trajectory in real time with optional consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The proposed algorithms are implemented on our quadrotor tail-sitter prototype, “Hong Hu,” and their effectiveness are demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10 m/s, typical tail-sitter maneuvers (transition, level flight, and loiter) with speed up to 20 m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight, and Cuban Eight) with acceleration up to 2.5 g. The video demonstration is available at https://youtu.be/2x_bLbVuyrk .

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