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meSch: Multi-Agent Energy-Aware Scheduling for Task Persistence

Kaleb Ben Naveed, An Dang, Rahul Kumar, Dimitra Panagou

TL;DR

meSch addresses persistent multi-robot missions with a single charging resource, including scenarios where the charging station is mobile and its position is uncertain. The framework runs online in short horizons, estimating rendezvous points via EKF, reserving energy for uncertainty, constructing candidate trajectories, and enforcing a gap and energy-aware scheduler comprising gware and eware. It supports nonlinear dynamics and varying discharge rates, with a scheduling complexity of $\\mathcal{O}(N \\log N)$ and validated through simulations and hardware experiments showing disciplined charging behavior and robustness to charger uncertainty. The work offers practical impact for long-duration missions by enabling scalable, real-time, energy-aware task persistence with mobile charging.

Abstract

This paper develops a scheduling protocol for a team of autonomous robots that operate on long-term persistent tasks. The proposed framework, called meSch, accounts for the limited battery capacity of the robots and ensures that the robots return to charge their batteries one at a time at the single charging station. The protocol is applicable to general nonlinear robot models under certain assumptions, does not require robots to be deployed at different times, and can handle robots with different discharge rates. We further consider the case when the charging station is mobile and its state information is subject to uncertainty. The feasibility of the algorithm in terms of ensuring persistent charging is given under certain assumptions, while the efficacy of meSch is validated through simulation and hardware experiments.

meSch: Multi-Agent Energy-Aware Scheduling for Task Persistence

TL;DR

meSch addresses persistent multi-robot missions with a single charging resource, including scenarios where the charging station is mobile and its position is uncertain. The framework runs online in short horizons, estimating rendezvous points via EKF, reserving energy for uncertainty, constructing candidate trajectories, and enforcing a gap and energy-aware scheduler comprising gware and eware. It supports nonlinear dynamics and varying discharge rates, with a scheduling complexity of and validated through simulations and hardware experiments showing disciplined charging behavior and robustness to charger uncertainty. The work offers practical impact for long-duration missions by enabling scalable, real-time, energy-aware task persistence with mobile charging.

Abstract

This paper develops a scheduling protocol for a team of autonomous robots that operate on long-term persistent tasks. The proposed framework, called meSch, accounts for the limited battery capacity of the robots and ensures that the robots return to charge their batteries one at a time at the single charging station. The protocol is applicable to general nonlinear robot models under certain assumptions, does not require robots to be deployed at different times, and can handle robots with different discharge rates. We further consider the case when the charging station is mobile and its state information is subject to uncertainty. The feasibility of the algorithm in terms of ensuring persistent charging is given under certain assumptions, while the efficacy of meSch is validated through simulation and hardware experiments.
Paper Structure (23 sections, 1 theorem, 19 equations, 7 figures, 2 tables, 3 algorithms)

This paper contains 23 sections, 1 theorem, 19 equations, 7 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Notation
  4. System Description
  5. Problem Statement
  6. Method Overview, Motivation & Key Ideas
  7. Method motivation
  8. Construction of Gap flags
  9. Multi-Agent Energy-Aware Scheduling
  10. Estimating Rendezvous Point
  11. Reserve Energy for Uncertainty-Aware Landing
  12. Construction of Candidate Trajectories
  13. Energy-aware Scheduling
  14. Gap-aware (gware)
  15. Energy-aware (eware)
  16. ...and 8 more sections

Key Result

Theorem 1

Suppose that at $j =0$ the Gap flag condition gap_flag and the Reserve SoC condition min_soc are satisfied. Therefore, the conditions eq:min_energy_constraints and eq:gap_lower_bound are satisfied for $[t_{0}, t^i_{0, R} )$. Following this, if solution exist for eq:landing_overall and eq:b2b_prob_ov

Figures (7)

  • Figure 1: meSch: Multi-Agent Energy-Aware Scheduling
  • Figure 2: This figure illustrates the generation of candidate trajectories at time $t_j$. All the candidate trajectories terminate at the rendezvous point $x^{rp}_j$ at time $t^i_{j,C}$.
  • Figure 3: Results from the Multi-Agent Energy-Aware Persistent Ergodic Search Case study
  • Figure 4: These plots show results for the scenarios when four quadrotors have different SoC capacities and different discharge rates. The plots validate that quadrotors always maintain the minimum of $(T_c + T_\delta)$ gap while visiting the charging station.
  • Figure 5: The communication architecture of the system.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Theorem 1
  • proof
  • proof