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Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks.

Omer Melih Gul1

  • 1Department of Computer Engineering, Bahcesehir University, 34349 Istanbul, Turkey.

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Summary

This study introduces energy-aware multi-robot task-allocation (MRTA) algorithms for energy-harvesting (EH) robots. The EH and Task-aware MRTA approach significantly conserves robot battery life, outperforming other methods.

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energy harvestingmulti-robot systemstask allocationwireless networks

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Area of Science:

  • Robotics
  • Wireless Sensor Networks
  • Energy Harvesting

Background:

  • Investigates energy-aware multi-robot task-allocation (MRTA) in clustered networks with energy-harvesting (EH) robots.
  • Focuses on a cluster with a base station and M+1 robots, where M tasks are assigned each round.

Purpose of the Study:

  • To optimally or near-optimally allocate M tasks to M robots considering travel distance, energy consumption, battery levels, and EH capabilities.
  • To develop and evaluate novel MRTA algorithms for energy-constrained robotic networks.

Main Methods:

  • Proposed three MRTA algorithms: Classical MRTA, Task-aware MRTA, and EH and Task-aware MRTA.
  • Evaluated algorithm performance using independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes.
  • Tested scenarios with 5 and 10 robots, each with the same number of tasks.

Main Results:

  • The EH and Task-aware MRTA Approach demonstrated superior performance.
  • This approach maintained up to 100% more battery energy compared to the Classical MRTA Approach.
  • It also conserved up to 20% more battery energy than the Task-aware MRTA Approach.

Conclusions:

  • The EH and Task-aware MRTA algorithm is highly effective for energy-aware task allocation in robotic networks.
  • This method significantly enhances energy efficiency and operational longevity of EH robots.
  • The findings are crucial for optimizing resource management in distributed robotic systems.