Motion planning and trajectory generation are crucial technologies in various domains including the control of Unmanned Aerial Vehicles, manipulators, and rockets. However, optimization-based real-time motion planning becomes increasingly challenging due to the problem's probable non-convexity and the inherent limitations of non-linear programming algorithms. Highly nonlinear dynamics, obstacle avoidance constraints, and non-convex inputs can exacerbate these difficulties. In order to enhance the robustness and reduce the computational burden, this paper proposes a two-layer trajectory generating algorithm for intelligent ground vehicles with convex optimization methods, aiming to provide real-time guarantees for trajectory optimization and to improve the calculate speed of motion prediction. Our approach involves breaking down the original problem into small horizon-based planning cycles with fixed final times, referred to as planning cycles. Each planning cycle is then solved within a series of restricted convex sets constructed by some customized search algorithms incrementally. We rigorously establish these advantages through mathematical analysis under moderate assumptions and comprehensive experimental validations. For linear vehicle models, comparative experiments with general sequential convex programming algorithms demonstrate the superior performance of our proposed method, particularly in terms of the computational efficiency in dynamic maps and the reduced final time.
View on arXiv@article{tan2025_2503.11072,
title={ A High-Speed Time-Optimal Trajectory Generation Strategy via a Two-layer Planning Model },
author={ Haotian Tan and Yuan-Hua Ni },
journal={arXiv preprint arXiv:2503.11072},
year={ 2025 }
}