Navigating fixed-wing unmanned aircraft presents significant challenges, primarily stemming from the intricate dynamics involved and the demand for rapid planning. In this paper, we introduce an efficient approach that concurrently addresses nonlinear control and trajectory planning challenges. To tackle the control problem, we introduce a computationally efficient motion primitive that effectively maps feasible targets to control inputs. As for trajectory planning, we propose an enhanced, rapidly exploring, random tree algorithm capable of generating high-quality trajectories with minimal exploration. Our primary motivation is to seamlessly integrate the introduced motion primitive with a sample-based trajectory-planning technique, providing a comprehensive solution to the intricate challenges posed by nonlinear control and trajectory planning for fixed-wing unmanned aircraft. Experimental results showcase our approach’s superiority, delivering enhanced control precision, faster planning speeds, and improved planning quality.
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