This paper presents a motion planning scheme we call Model Predictive Planning (MPP), designed to optimize trajectories through obstacle-laden environments. The approach involves path planning, trajectory refinement through the solution of a quadratic program, and real-time selection of optimal trajectories. The paper highlights three technical innovations: a raytracing-based path-to-trajectory refinement, the integration of this technique with a multi-path planner to overcome difficulties due to local minima, and a method to achieve timescale separation in trajectory optimization. The scheme is demonstrated through simulations on a 2D longitudinal aircraft model and shows strong obstacle avoidance performance.

翻译：本文提出一种名为模型预测规划（MPP）的运动规划方案，旨在优化穿越障碍密集环境下的轨迹。该方法包括路径规划、通过二次规划求解实现轨迹精化，以及实时选择最优轨迹。本文突出三项技术创新：基于光线追踪的路径到轨迹精化技术、将该技术与多路径规划器结合以克服局部极小值困难的方法，以及实现轨迹优化中时间尺度分离的技术。该方案通过在二维纵轴飞机模型上的仿真验证，展现出强大的避障性能。