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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Related Experiment Video

Updated: Aug 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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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.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
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.

Keywords:
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.