Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
This paper proposes a distributed, risk-aware multi-agent NBV framework where each robot maintains a private local 3D Gaussian Splatting map and jointly maximizes expected information gain via Consensus ADMM, reducing communication by orders of magnitude while maintaining mapping quality and safety.
[2606.04158] Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
[Submitted on 2 Jun 2026]
Title:Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
View a PDF of the paper titled Multi-Agent Next-Best-View Optimization for Risk-Averse Planning, by Amirhossein Mollaei Khass and 5 other authors
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Abstract:Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficient coordination. Centralized approaches rely on sharing raw sensor data or significant communication overhead, resulting in limited scalability. We propose a distributed, risk-aware multi-agent NBV framework in which each robot maintains a private local 3D Gaussian Splatting map and the team jointly maximizes expected information gain (EIG) restricted to masked zones along planned trajectories. The resulting distributed objective is solved by Consensus ADMM (C-ADMM) over a communication graph, with each robot exchanging only candidate viewpoints, planned trajectory descriptors, and scalar EIG contributions. Collision risk along each trajectory is modeled via Average Value-at-Risk (AV@R) over the local 3DGS map and used both to shape the masking radius and to score planned paths. Experiments in Gibson environments at multiple team sizes show that the distributed formulation approaches the centralized baseline in mapping quality and trajectory safety while reducing communication by orders of magnitude.
Comments: 8 pages, 5 figures. Submitted to IROS 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.04158 [cs.RO]
(or arXiv:2606.04158v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.04158
arXiv-issued DOI via DataCite
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From: Amirhossein Mollaei Khass [view email] [v1] Tue, 2 Jun 2026 19:23:23 UTC (4,266 KB)
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