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The Persistent Robot Charging Problem for Long-Duration Autonomy

Nitesh Kumar, Jaekyung Jackie Lee, Sivakumar Rathinam, Swaroop Darbha, P. B. Sujit, Rajiv Raman

TL;DR

This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots.

Abstract

This paper introduces a novel formulation aimed at determining the optimal schedule for recharging a fleet of $n$ heterogeneous robots, with the primary objective of minimizing resource utilization. This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots. A novel Integer Linear Programming (ILP) model is proposed to calculate the optimal initial conditions (partial charge) for individual robots, leading to the minimal utilization of charging stations. This formulation was further generalized to maximize the servicing time for robots given adequate charging stations. The efficacy of the proposed formulation is evaluated through a comparative analysis, measuring its performance against the thrift price scheduling algorithm documented in the existing literature. The findings not only validate the effectiveness of the proposed approach but also underscore its potential as a valuable tool in optimizing resource allocation for a range of robotic and engineering applications.

The Persistent Robot Charging Problem for Long-Duration Autonomy

TL;DR

This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots.

Abstract

This paper introduces a novel formulation aimed at determining the optimal schedule for recharging a fleet of heterogeneous robots, with the primary objective of minimizing resource utilization. This study provides a foundational framework applicable to Multi-Robot Mission Planning, particularly in scenarios demanding Long-Duration Autonomy (LDA) or other contexts that necessitate periodic recharging of multiple robots. A novel Integer Linear Programming (ILP) model is proposed to calculate the optimal initial conditions (partial charge) for individual robots, leading to the minimal utilization of charging stations. This formulation was further generalized to maximize the servicing time for robots given adequate charging stations. The efficacy of the proposed formulation is evaluated through a comparative analysis, measuring its performance against the thrift price scheduling algorithm documented in the existing literature. The findings not only validate the effectiveness of the proposed approach but also underscore its potential as a valuable tool in optimizing resource allocation for a range of robotic and engineering applications.
Paper Structure (14 sections, 17 equations, 7 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 17 equations, 7 figures, 1 table, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. PROBLEM STATEMENT
  3. SCHEDULING FRAMEWORK
  4. Finding a Scheduling Horizon
  5. Minimizing resources
  6. Maximizing flying time
  7. ROBUST SCHEDULING
  8. Safety margins
  9. Managing Delay
  10. COMPUTATIONAL RESULTS
  11. Minimizing resources
  12. Finding a new scheduling horizon with safety margins
  13. Simulation results
  14. CONCLUSIONS

Figures (7)

  • Figure 1: Evolution of robot's states: Illustration of the charging (green) and discharging (red) phases as the robot transitions from one state to the next.
  • Figure 2: State Transition Schematic: Depiction of the cyclic evolution of robot's states throughout the charging cycle. The transition initiates with charging, indicated by the green segment, progressing clockwise. Upon reaching full charge, the state transitions clockwise into the red segment.
  • Figure 3: Graph representing the candidate cycle times
  • Figure 4: Performance of the Integer Linear Programming-based Scheduling (ILPS) algorithm and Thrift Price Window Scheduling (TPWS) algorithm.
  • Figure 5: Comparison between Original Operational Time and Reduced Operational Time.
  • ...and 2 more figures