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

TL;DR

Results apply the proposed methodology to coupled motion and energy utilization planning problems for a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and an electric package delivery drone that must track waysets with both position and battery state of charge requirements.

Abstract

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.

Figures (7)

• Figure 1: Linear, quasi-steady approximation of the velocity and power relationship.
• Figure 2: Projections of the constrained zonotope $\mathcal{Z}_{cx}$ representing coupled constraints on the energy and motion states. The bottom sub-figure shows the case where the forward progress constraint \ref{['eq:xidot-geq-vmin']} is imposed.
• Figure 3: Case Study 1: Planned trajectory for a hybrid-electric UAS. The red square is the start position and the green star corresponds to the reference state $\mathbf{x}_N^r = \mathbf{x}_k^r \; \forall k \in \{0, ..., N-1\}$. The blue dots are the planned vehicle trajectory.
• Figure 4: Case Study 1: Planned motion states and inputs for the hybrid-electric UAS example. Red points indicate time steps where the vehicle is in the noise-restricted area.
• Figure 5: Case Study 1: Planned energy states for the hybrid-electric UAS example. Red points indicate time steps where the vehicle is in the noise-restricted area.