Nonlinear Trajectory Optimization Models for Energy-Sharing UAV-UGV Systems with Multiple Task Locations

TL;DR

This paper introduces a smooth nonlinear programming model for energy-sharing UAV-UGV systems that efficiently optimizes trajectories and task scheduling, supporting terrain constraints and partial recharging.

Contribution

It presents a novel smooth nonlinear model that simplifies trajectory optimization by avoiding integer programming, enabling faster solutions for UAV-UGV systems.

Findings

Reduces computation time by orders of magnitude compared to mixed-integer models.

Supports partial UAV recharging and terrain access constraints.

Demonstrates effectiveness on a system with multiple task locations.

Abstract

Energy-sharing UAV-UGV systems extend the endurance of Uncrewed Aerial Vehicles (UAVs) by leveraging Uncrewed Ground Vehicles (UGVs) as mobile charging stations, enabling persistent autonomy in infrastructure-sparse environments. Trajectory optimization for these systems is often challenging due to UGVs' terrain access constraints and the discrete nature of task scheduling. We propose a smooth nonlinear program model for the joint trajectory optimization for these systems. Unlike existing models, the proposed model allows smooth parameterization of UGVs' terrain access constraints and supports partial UAV recharging. Further, it introduces a smooth approximation of disjunctive constraints that eliminates the need for computationally expensive integer programming and enables efficient solutions via nonlinear programming algorithms. We demonstrate the proposed model on a one-UAV-one-UGV system with multiple task locations. Compared with mixed-integer nonlinear programs, this model reduces the computation time by orders of magnitude.