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Summary

This study introduces a novel energy-efficient navigation method for robot swarms, optimizing collective energy use. Leading robots share path discoveries, reducing travel distance for individual robots and the entire swarm.

Keywords:
collaborative sensingcollision avoidanceenergy efficientleader–followermulti-agent systemsswarm intelligenceswarm robotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Swarm Intelligence

Background:

  • Collaborative robots are crucial in industrial applications.
  • Energy-efficient navigation in unknown environments is a growing concern for autonomous robots.
  • Existing path planning methods often focus on individual robot efficiency.

Purpose of the Study:

  • To develop a novel methodology for low-overhead collaborative sensing, real-time mapping, and localization for robot swarms.
  • To optimize energy consumption for the entire robot swarm, not just individual robots.
  • To enhance navigation efficiency and reduce energy expenditure during swarm movement.

Main Methods:

  • Proposed an energy- and information-aware management algorithm for swarm navigation.
  • Modified the Partial Swarm SLAM (Simultaneous Localization and Mapping) technique.
  • Implemented a system where leading robots share discovered optimal paths and environmental information with follower robots.

Main Results:

  • The proposed algorithm effectively reduced energy consumption for the swarm as a whole.
  • Simulation results demonstrated robots re-optimizing solutions and sharing information within the swarm.
  • Comparative analysis showed a 13% reduction in traveling distance for individual robots and up to 11% for the swarm.

Conclusions:

  • The novel approach enables efficient navigation and energy optimization for robot swarms in unknown environments.
  • Collaborative sensing and information sharing are key to improving swarm-level energy efficiency.
  • The method offers a practical solution for energy-conscious deployment of autonomous robot swarms.