模型预测规划：低机动性飞行器在障碍物密集环境中的轨迹规划
Model Predictive Planning: Trajectory Planning in Obstruction-Dense Environments for Low-Agility Aircraft
AI总结：
针对低机动性飞行器在密集障碍环境中轨迹规划易出现优化不可行的问题，提出包含多路径规划、光线追踪约束生成和凸二次规划的MPP方法，通过多候选路径提升可行轨迹概率，并在纵向飞行器模型上验证有效性。
摘要：
我们提出了模型预测规划（Model Predictive Planning，MPP），这是一种面向固定翼飞行器等低机动性载具的轨迹规划器，用于在充满障碍物的环境中导航。MPP由三部分组成：（1）识别候选路径的多路径规划流程；（2）围绕这些路径生成线性约束以实现避障的光线追踪流程；（3）在这些约束下寻找可行轨迹（若存在）的凸二次规划模块。低机动性飞行器无法追踪任意路径，因此将给定路径细化为既符合载具有限机动性又能避障的轨迹，往往会导致优化问题不可行。MPP的核心特性是在细化过程中高效考量多条候选路径，从而大幅提升找到可行且可追踪轨迹的概率。我们在纵向飞行器模型上验证了MPP的有效性。
英文摘要：
We present Model Predictive Planning (MPP), a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments. MPP consists of (1) a multi-path planning procedure that identifies candidate paths, (2) a raytracing procedure that generates linear constraints around these paths to enforce obstacle avoidance, and (3) a convex quadratic program that finds a feasible trajectory within these constraints if one exists. Low-agility aircraft cannot track arbitrary paths, so refining a given path into a trajectory that respects the vehicle's limited maneuverability and avoids obstacles often leads to an infeasible optimization problem. The critical feature of MPP is that it efficiently considers multiple candidate paths during the refinement process, thereby greatly increasing the chance of finding a feasible and trackable trajectory. We demonstrate the effectiveness of MPP on a longitudinal aircraft model.
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