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

TL;DR

This paper introduces an energy-aware predictive motion planning approach for autonomous vehicles using hybrid zonotope constraints within a model predictive control framework, enabling real-time energy and motion optimization.

Contribution

It presents a novel hybrid zonotope-based constraint representation integrated into MPC for coupled energy and motion planning in autonomous systems.

Findings

Real-time implementation of mixed-integer MPC for energy-aware planning

Successful application to hybrid-electric vehicles with noise restrictions

Effective planning for electric drones with position and battery constraints

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.