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.

翻译：无人飞行系统的能量与运动动力学紧密耦合，必须由机载规划算法予以考虑。本研究提出一种利用模型预测控制（MPC）实现运动与能量协同规划的策略。首先建立了耦合能量与运动动力学的降阶线性时不变模型。采用约束zonotope表示状态与输入约束，并利用混合zonotope表征与环境地图相关的非凸约束。这些约束表示的结构特性在专为MPC运动规划问题设计的混合整数二次规划求解器中得到充分利用。研究结果将所提方法应用于两类运动与能量协同规划问题：1）混合动力飞行器在噪声限制区域上空飞行时必须限制发动机使用；2）电动包裹配送无人机需同时满足航路点位置与电池荷电状态要求。通过采用结构优化求解器，所提出的混合整数MPC框架可实现实时部署。