用混合区域表示法实现无人机实时能量-运动协同规划
Energy-Aware Predictive Motion Planning for Autonomous Vehicles Using a Hybrid Zonotope Constraint Representation
- 用简化线性模型耦合能量与运动动态，结合混合区域表征约束
- 在真实飞行场景中实现电池电量与噪音限制的联合优化，计算延迟低于100毫秒
- 适合需要长续航与环境约束的电动飞行器任务规划
无人飞行系统具有紧密耦合的能量与运动动力学，需由机载规划算法予以考虑。本文提出一种基于模型预测控制（MPC）的耦合运动与能量规划策略。构建了耦合能量与运动动态的降阶线性时不变模型。采用约束区域表示状态与输入约束，混合区域用于表征与环境地图相关的非凸约束。利用这些约束表示的结构特性，设计了针对MPC运动规划问题的混合整数二次规划求解器。将该方法应用于两类实际场景：1）需在噪声敏感区域限制发动机使用的混合电动车辆；2）需同时满足位置与电池荷电状态要求的电动快递无人机路径跟踪。通过结构化求解器，所提混合整数MPC可实现实时部署，计算延迟低于100毫秒。
原文摘要 · Abstract (English)
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.
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