Research on Trajectory Generation Method for Multi-Objective Optimization of Thrust Vector Vehicle in Constrained Space

Abstract

Thrust-vectoring UAVs can realize decoupling of position and attitude compared with conventional quadrotors due to the ability to change thrust direction, and are used to perform various complex indoor and outdoor missions. However, existing trajectory generation frameworks are mostly for quadrotors with fixed thrust direction and a coplanar surface, and do not consider the dynamics of thrust-vectoring UAVs. To address this, this paper proposes a multi-objective trajectory generation method for thrust-vectoring UAVs in constraint space. By parametrically modeling the constraint space, the method considers the effects of environmental boundary constraints and platform dynamics characteristics on the collision constraints and motion decoupling of the trajectory, and comprehensively optimizes the trajectory’s indicators of stability, speed, and safety to plan the states and input actions of the flight trajectory. Meanwhile, a trajectory generation evaluation system is proposed, given that compared with the conventional quadratic objective function, the proposed method is effective in reducing the attitude change of the trajectory, improving the rapidity and safety, in which LθL_{\theta}Lθ​ and LriskL_{r i s k}Lrisk​ are reduced by 70.4% and 19.1%, respectively. Meanwhile, by comparing with the conventional quadrotor, the advantages of the thrust-vectoring in decoupling motion are quantified, especially in reducing the attitude change during flight, the pitch angle of the generated trajectory is reduced from ±30° to within ±20° degrees, which exerts the motion decoupling advantages of the thrust-vectoring.

Keywords

Constraint space / Thrust vector vehicle / Trajectory generation / Multi-objective optimization

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used ChatGPT for language polishing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions

Y.S.: Software, Formal analysis, Writing—Original Draft. W.W.: Conceptualization, Methodology, Supervision, Resources. M.D.: Investigation, Writing—Review & Editing. Y.W.: Investigation, Validation. X.Z.: Data Curation. J.L.: Investigation.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Funding

This research received no external funding. The APC was funded by the Beijing Natural Science Foundation (3244036), the National Key Research and Development Program of China (2020YFC1512500), the Shandong Postdoctoral Innovation Project (SDCX-ZG-202303052), and the General Program of the Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX1101).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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