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Action-Consistent Decentralized Belief Space Planning with Inconsistent Beliefs

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This research introduces decentralized multi-robot belief space planning (MR-BSP) algorithms designed to ensure safe decision-making under inconsistent beliefs and limited data sharing. The authors present VerifyAC, which guarantees consistent joint action selection via a three-step verification process, triggering communication only when necessary. Extensions R-VerifyAC and R-VerifyAC-simp further reduce communication frequency and improve computation time by relaxing consistency criteria and verifying partial observations. Theoretical guarantees are supported by discrete simulations, while experimental results demonstrate applicability to continuous, high-dimensional state spaces using active multi-robot visual SLAM with real robots.

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Latest development2026-11-03 05:00 UTC
Action-Consistent Decentralized Belief Space Planning with Inconsistent Beliefs and Limited Data Sharing: Framework and Simplification Algorithms with Formal Guarantees

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11/3
  1. arXiv Robotics — research abstractsSignal
    Action-Consistent Decentralized Belief Space Planning with Inconsistent Beliefs and Limited Data Sharing: Framework and Simplification Algorithms with Formal Guarantees

    This research introduces decentralized multi-robot belief space planning (MR-BSP) algorithms designed to ensure safe decision-making under inconsistent beliefs and limited data sharing. The authors present VerifyAC, which guarantees consistent joint action selection via a three-step verification process, triggering communication only when necessary. Extensions R-VerifyAC and R-VerifyAC-simp further reduce communication frequency and improve computation time by relaxing consistency criteria and verifying partial observations. Theoretical guarantees are supported by discrete simulations, while experimental results demonstrate applicability to continuous, high-dimensional state spaces using active multi-robot visual SLAM with real robots.

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