Risk-aware UAV-UGV Rendezvous with Chance-Constrained Markov Decision Process

TL;DR

This paper introduces a novel chance-constrained Markov Decision Process framework to optimize UAV-UGV routing under stochastic energy consumption, minimizing expected travel time while ensuring low risk of UAV power depletion.

Contribution

It is the first to formulate UAV-UGV routing as a CCMDP considering power consumption uncertainty and solves it using LP for optimal policies.

Findings

Effective in ISR mission scenarios.

Reduces risk of UAV power failure.

Optimizes rendezvous timing and locations.

Abstract

We study a chance-constrained variant of the cooperative aerial-ground vehicle routing problem, in which an Unmanned Aerial Vehicle (UAV) with limited battery capacity and an Unmanned Ground Vehicle (UGV) that can also act as a mobile recharging station need to jointly accomplish a mission such as monitoring a set of points. Due to the limited battery capacity of the UAV, two vehicles sometimes have to deviate from their task to rendezvous and recharge the UAV\@. Unlike prior work that has focused on the deterministic case, we address the challenge of stochastic energy consumption of the UAV\@. We are interested in finding the optimal policy that decides when and where to rendezvous such that the expected travel time of the UAV is minimized and the probability of running out of charge is less than a user-defined tolerance. We formulate this problem as a Chance Constrained Markov Decision Process (CCMDP). To the best knowledge of the authors, this is the first CMDP-based formulation for the UAV-UGV routing problems under power consumption uncertainty. We adopt a Linear Programming (LP) based approach to solve the problem optimally. We demonstrate the effectiveness of our formulation in the context of an Intelligence Surveillance and Reconnaissance (ISR) mission.