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：无人驾驶航空系统具有紧密耦合的能量与运动动力学特性，机载规划算法必须对此加以考虑。本研究提出一种利用模型预测控制实现运动与能量协同规划的框架。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。针对MPC运动规划问题定制的混合整数二次规划求解器充分利用了这些约束表示的结构特性。研究结果将所提方法应用于两类协同规划问题：1）混合动力飞行器在噪声限制区域需限制发动机使用的场景，2）需同时满足航路点位置与电池荷电状态要求的电动包裹配送无人机。通过采用结构优化求解器，所提出的混合整数MPC框架可实现实时运算。