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Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration.

Huei-Yung Lin1, Yi-Chun Huang2

  • 1Department of Electrical Engineering, Advanced Institute of Manufacturing with High-Tech Innovation, National Chung Cheng University, Chiayi 621, Taiwan.

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Summary
This summary is machine-generated.

This study introduces collaborative complete coverage and path planning (CCPP) algorithms for mobile robots. The new method enhances exploration efficiency in unknown environments for both single and multi-robot systems.

Keywords:
complete coveragemobile robotmulti-robot systempath planning

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Area of Science:

  • Mobile Robotics
  • Artificial Intelligence
  • Path Planning Algorithms

Background:

  • Exploration of unknown environments is crucial for mobile robotics in consumer and military applications.
  • Complete Coverage and Path Planning (CCPP) is a key area of recent research for robot navigation.
  • Existing CCPP methods may not fully optimize collaborative exploration in complex environments.

Purpose of the Study:

  • To present novel collaborative CCPP algorithms for both single-robot and multi-robot systems.
  • To enhance the efficiency of unknown environment exploration using mobile robots.
  • To introduce a new cost function and goal selection mechanism for optimized coverage.

Main Methods:

  • Developed a new cost function to maximize incremental coverage from robot movement.
  • Designed a goal selection function to facilitate collaborative exploration in multi-robot systems.
  • Integrated local gains (individual robots) and global gain (goal selection) for optimized coverage efficiency.

Main Results:

  • CCPP algorithms were tested on various unknown and complex environment maps.
  • Simulation results demonstrated the effectiveness of the proposed collaborative CCPP technique.
  • Performance evaluation confirmed improved coverage efficiency in multi-robot scenarios.

Conclusions:

  • The proposed collaborative CCPP algorithms are effective for mobile robot navigation in unknown environments.
  • The integration of local and global gains optimizes overall coverage efficiency.
  • This research advances the capabilities of mobile robots in exploration and mapping tasks.