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Energy-Aware Routing Algorithm for Mobile Ground-to-Air Charging

Bill Cai, Fei Lu, Lifeng Zhou

TL;DR

The paper tackles energy-constrained planning for a cooperative system of a UGV and a UAV, where the UGV serves as a mobile base and charging station for persistent site surveying. It formulates an energy-constrained routing problem and solves it with a two-stage method: first a TSP solver yields a guided survey tour, then Monte-Carlo Tree Search (MCTS) refines the tour by allocating discrete UAV energy levels at each site to minimize $T_{ ext{total}}$ under energy constraints. Key modeling elements include joint energy costs $C_{ ext{g}}$, $C_{ ext{ga}}$, $C_{ ext{a}}$, $C_{ ext{s}}$, speeds $V_{ ext{g}}$, $V_{ ext{a}}$, UAV charging rate $R_c$, and a geometric representation where UAV energy allocations map to a circle of radius $r = rac{e_{ ext{a}} - C_{ ext{s}} T_{ ext{survey}}}{2 C_{ ext{a}}}$ around each site, with rendezvous at a chord intersection. Experiments with simulations and a proof-of-concept using a Clearpath Husky and ModalAI Sentinel demonstrate near-optimal mission times and fast runtimes up to 50 sites, indicating practicality for real-world energy-aware persistent surveillance and charging-enabled missions.

Abstract

We investigate the problem of energy-constrained planning for a cooperative system of an Unmanned Ground Vehicles (UGV) and an Unmanned Aerial Vehicle (UAV). In scenarios where the UGV serves as a mobile base to ferry the UAV and as a charging station to recharge the UAV, we formulate a novel energy-constrained routing problem. To tackle this problem, we design an energy-aware routing algorithm, aiming to minimize the overall mission duration under the energy limitations of both vehicles. The algorithm first solves a Traveling Salesman Problem (TSP) to generate a guided tour. Then, it employs the Monte-Carlo Tree Search (MCTS) algorithm to refine the tour and generate paths for the two vehicles. We evaluate the performance of our algorithm through extensive simulations and a proof-of-concept experiment. The results show that our algorithm consistently achieves near-optimal mission time and maintains fast running time across a wide range of problem instances.

Energy-Aware Routing Algorithm for Mobile Ground-to-Air Charging

TL;DR

The paper tackles energy-constrained planning for a cooperative system of a UGV and a UAV, where the UGV serves as a mobile base and charging station for persistent site surveying. It formulates an energy-constrained routing problem and solves it with a two-stage method: first a TSP solver yields a guided survey tour, then Monte-Carlo Tree Search (MCTS) refines the tour by allocating discrete UAV energy levels at each site to minimize under energy constraints. Key modeling elements include joint energy costs , , , , speeds , , UAV charging rate , and a geometric representation where UAV energy allocations map to a circle of radius around each site, with rendezvous at a chord intersection. Experiments with simulations and a proof-of-concept using a Clearpath Husky and ModalAI Sentinel demonstrate near-optimal mission times and fast runtimes up to 50 sites, indicating practicality for real-world energy-aware persistent surveillance and charging-enabled missions.

Abstract

We investigate the problem of energy-constrained planning for a cooperative system of an Unmanned Ground Vehicles (UGV) and an Unmanned Aerial Vehicle (UAV). In scenarios where the UGV serves as a mobile base to ferry the UAV and as a charging station to recharge the UAV, we formulate a novel energy-constrained routing problem. To tackle this problem, we design an energy-aware routing algorithm, aiming to minimize the overall mission duration under the energy limitations of both vehicles. The algorithm first solves a Traveling Salesman Problem (TSP) to generate a guided tour. Then, it employs the Monte-Carlo Tree Search (MCTS) algorithm to refine the tour and generate paths for the two vehicles. We evaluate the performance of our algorithm through extensive simulations and a proof-of-concept experiment. The results show that our algorithm consistently achieves near-optimal mission time and maintains fast running time across a wide range of problem instances.
Paper Structure (2 sections, 1 figure)

This paper contains 2 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Problem formulation

Figures (1)

  • Figure 1: A cooperative autonomous platform (a) consisting of a UGV (Clearpath Husky rover) and a UAV (ModalAI Sentinel drone) is tasked to survey five sites (blue dots) scattered on the Drexel Park (b).