Connectivity-Preserving Distributed Informative Path Planning for Mobile Robot Networks

Binh Nguyen, Truong X. Nghiem, Linh Nguyen, Hung M. La, Thang Nguyen

Research output: Contribution to journalArticlepeer-review

Abstract

This letter addresses the distributed informative path planning (IPP) problem for a mobile robot network to optimally explore a spatial field. Each robot is able to gather noisy environmental measurements while navigating the environment and build its own model of a spatial phenomenon using the Gaussian process and local data. The IPP optimization problem is formulated in an informative way through a multi-step prediction scheme constrained by connectivity preservation and collision avoidance. The shared hyperparameters of the local Gaussian process models are also arranged to be optimally computed in the path planning optimization problem. By the use of the proximal alternating direction method of multiplier, the optimization problem can be effectively solved in a distributed manner. It theoretically proves that the connectivity in the network is maintained over time whilst the solution of the optimization problem converges to a stationary point. The effectiveness of the proposed approach is verified in synthetic experiments by utilizing a real-world dataset.

Original languageEnglish (US)
Pages (from-to)2949-2956
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume9
Issue number3
DOIs
StatePublished - Mar 1 2024
Externally publishedYes

Keywords

  • Path planning for multiple mobile robots or agents
  • distributed learning
  • distributed robot systems
  • informative path planning
  • integrated planning and learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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