Path Planning and Energy Management of Hybrid Air Vehicles for Urban Air Mobility

TL;DR

This paper introduces a coupled path planning and energy management algorithm for hybrid air vehicles operating in urban environments, optimizing routes and battery use under noise restrictions and energy constraints.

Contribution

It develops a novel sampling-based algorithm for near-optimal joint path and energy mode planning in hybrid air vehicles, addressing a coupled discrete-continuous decision problem.

Findings

Feasible solutions are within 15% of the lower bounds on average.

The algorithm effectively manages battery constraints and noise restrictions.

Numerical simulations validate the approach's efficacy.

Abstract

A novel coupled path planning and energy management problem for a hybrid unmanned air vehicle is considered, where the hybrid vehicle is powered by a dual gas/electric system. Such an aerial robot is envisioned for use in an urban setting where noise restrictions are in place in certain zones necessitating battery only operation. We consider the discrete version of this problem, where a graph is constructed by sampling the boundaries of the restricted zones, and develop a path planning algorithm. The planner simultaneously solves the path planing along with the energy mode switching control, under battery constraints and noise restrictions. This is a coupled problem involving discrete decision making to find the path to travel, and determining the state of charge of the battery along the path, which is a continuous variable. A sampling based algorithm to find near optimal solution to this problem is presented. To quantify the efficacy of the solution, an algorithm that computes tight lower bounds is also presented. The algorithms presented are verified using numerical simulations, and the average gap between the feasible solutions (upper bounds) and the lower bounds are, empirically, shown to be within 15% of each other.