Heterogeneous Vehicle Routing and Teaming with Gaussian Distributed Energy Uncertainty

TL;DR

This paper introduces a stochastic programming framework for heterogeneous vehicle routing under Gaussian-distributed energy uncertainty, optimizing energy costs in complex, uncertain environments.

Contribution

It presents a novel approach combining Gaussian process regression with stochastic programming for heterogeneous vehicle routing problems.

Findings

Effective energy cost minimization demonstrated

Framework handles heterogeneity and uncertainty

Validated through computational experiments

Abstract

For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and heterogeneous vehicles and tasks. We represent the heterogeneity as linear constraints, estimate the uncertain energy cost through Gaussian process regression, formulate this stochasticity as chance constraints or stochastic recourse costs, and then solve the stochastic programs using branch and cut algorithms to minimize the expected energy cost. The performance and practicality are demonstrated through extensive computational experiments and a practical test case.