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Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints.

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Summary
This summary is machine-generated.

This study introduces a new multi-robot information gathering (MR-IG) algorithm that avoids state discretization and handles robot constraints. The novel approach enables efficient data collection for mobile robotic sensors in complex environments.

Keywords:
Gaussian processesdistributed multi-agent systemsinformation gatheringrobotics

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Networks

Background:

  • Information gathering (IG) algorithms are crucial for mobile robotic sensors to reconstruct physical processes.
  • Existing multi-robot IG algorithms often struggle with state discretization and inter-robot constraints like collision avoidance.

Purpose of the Study:

  • To present a novel multi-robot information gathering (MR-IG) algorithm that overcomes the limitations of state discretization and inter-robot constraints.
  • To enable efficient and coordinated data collection for mobile robotic systems with complex dynamics.

Main Methods:

  • Utilized an underlying model of the physical process and sampling-based planners for continuous path planning.
  • Employed the max-sum algorithm for distributed decision-making, optimizing an information-theoretic utility function.
  • Integrated collision avoidance and communication constraints into the decision-making process.

Main Results:

  • The proposed MR-IG algorithm effectively handles continuous state spaces and inter-robot constraints.
  • Simulations and a field experiment with three quadcopters demonstrated the approach's effectiveness.
  • Results showed scalability with an increasing number of robots.

Conclusions:

  • The novel MR-IG approach provides an efficient and scalable solution for coordinated information gathering by mobile robots.
  • This method advances the capabilities of robotic sensor networks in complex, dynamic environments.