标题： 基于混合zonotope约束表示的自动驾驶汽车节能预测运动规划 显示英文标题

标题： Energy-Aware Predictive Motion Planning for Autonomous Vehicles Using a Hybrid Zonotope Constraint Representation

摘要： 无人飞行系统具有紧密耦合的能量和运动动态特性，这些特性必须由机载规划算法考虑。 本研究提出了一种利用模型预测控制（MPC）进行耦合运动和能量规划的策略。 本文展示了一个降阶线性时不变的耦合能量和运动动态模型。 受限的zonotopes用于表示状态和输入约束，而混合zonotopes用于表示与环境地图相关的非凸约束。 这些约束表示的结构被利用于针对MPC运动规划问题定制的混合整数二次规划求解器中。 结果表明，所提出的策略适用于以下两个耦合运动和能量利用规划问题：1）混合动力电动汽车，在飞越有噪音限制的区域时必须限制发动机的使用；2）电动快递无人机，必须同时满足位置和电池电量状态的需求来跟踪航路点。 通过利用结构探索求解器，所提出的混合整数MPC公式可以在实时中实现。 显示英文摘要

摘要： Uncrewed aerial systems have tightly coupled energy and motion dynamics which must be accounted for by onboard planning algorithms. This work proposes a strategy for coupled motion and energy planning using model predictive control (MPC). A reduced-order linear time-invariant model of coupled energy and motion dynamics is presented. Constrained zonotopes are used to represent state and input constraints, and hybrid zonotopes are used to represent non-convex constraints tied to a map of the environment. The structures of these constraint representations are exploited within a mixed-integer quadratic program solver tailored to MPC motion planning problems. Results apply the proposed methodology to coupled motion and energy utilization planning problems for 1) a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and 2) an electric package delivery drone that must track waysets with both position and battery state of charge requirements. By leveraging the structure-exploiting solver, the proposed mixed-integer MPC formulations can be implemented in real time.