PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments

TL;DR

PRO-SPECT introduces a probabilistic planning algorithm for UAV-UGV teams that ensures energy safety in stochastic environments, balancing efficiency and risk with theoretical guarantees.

Contribution

It models travel times as random variables and develops a polynomial-time algorithm for risk-bounded planning, supporting both offline and online re-planning.

Findings

PRO-SPECT guarantees risk bounds in stochastic environments.

The algorithm supports real-time re-planning during missions.

Numerical simulations demonstrate improved safety and efficiency.

Abstract

We consider energy-aware planning for an unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) team operating in a stochastic environment. The UAV must visit a set of air points in minimum time while respecting energy constraints, relying on the UGV as a mobile charging station. Unlike prior work that assumed deterministic travel times or used fixed robustness margins, we model travel times as random variables and bound the probability of failure (energy depletion) across the entire mission to a user-specified risk level. We formulate the problem as a Mixed-Integer Program and propose PRO-SPECT, a polynomial-time algorithm that generates risk-bounded plans. The algorithm supports both offline planning and online re-planning, enabling the team to adapt to disturbances while preserving the risk bound. We provide theoretical results on solution feasibility and time complexity. We also demonstrate the performance of our method via numerical comparisons and simulations.