Abstract
Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.
| Original language | English |
|---|---|
| Pages (from-to) | 5344-5359 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Robotics |
| Volume | 41 |
| DOIs | |
| State | Published - 2025 |
Funding
Received 31 October 2024; revised 9 May 2025; accepted 17 July 2025. Date of publication 28 August 2025; date of current version 16 September 2025. This work was supported by Clemson University’s Virtual Prototyping of Autonomy Enabled Ground Systems under Grant W56HZV-21-2-0001 with the U.S. Army DEVCOM Ground Vehicle Systems Center. This work has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. This article was recommended for publication by Associate Editor W. Wan and Editor J. O’Kane upon evaluation of the reviewers’ comments. (Corresponding author: Nathan Goulet.) Nathan Goulet is with the Buildings and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA (e-mail: [email protected]).
Keywords
- Energy constraints
- intelligent vehicles path planning
- mobile robots
- optimal scheduling
- predictive control
- route planning
- uncertainty
- vehicle routing