Anisotropic Diffusion-Driven Ergodic Coverage in Multi-Robot Systems
Researchers propose a new anisotropic diffusion method for ergodic search in multi-robot systems, overcoming the uniform error propagation of traditional isotropic diffusion, using Perona-Malik diffusion gradient to guide robot motion for more flexible coverage.
Article intelligence
Key points
- Traditional ergodic search uses isotropic diffusion (heat equation), causing uniform error propagation in all directions.
- The new method introduces anisotropic diffusion (Perona-Malik), using gradient to guide robot motion for more flexible matching of target distribution.
- This approach generalizes previous results based on radial basis functions and heat equation, validated in multiple simulation scenarios.
Why it matters
This matters because traditional ergodic search uses isotropic diffusion (heat equation), causing uniform error propagation in all directions.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.24125] Anisotropic Diffusion-Driven Ergodic Coverage in Multi-Robot Systems
[Submitted on 22 May 2026]
Title:Anisotropic Diffusion-Driven Ergodic Coverage in Multi-Robot Systems
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Abstract:We consider the problem of combining potential field and ergodic search on multi-robot systems. Traditional ergodic search algorithms use metrics for ergodicity that account for the desired distribution at different scales. Recently, a heat equation-driven ergodic approach was proposed, which adds flexibility to the smoothing of the ergodic metric. However, such an approach, as it is an isotropic diffusion, propagates the error uniformly in all directions, regardless of changes in the desired distribution. We introduce a general class of anisotropic diffusion formulation of the ergodicity problem, which generates a potential field for the ergodic search. We demonstrate that this approach generalizes previous results, which consider radial basis functions and the solution of the heat equation to represent the difference between the goal density distribution and the covered trajectories. In our solution, the agent movement is directed using the gradient of the solution of the Perona-Malik diffusion, and our formulation includes the heat equation as a special case. We demonstrate the methodology with a series of simulations in different scenarios.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.24125 [cs.RO]
(or arXiv:2605.24125v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.24125
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1109/MRS66243.2025.11357259
DOI(s) linking to related resources
Submission history
From: Thales Costa Silva [view email] [v1] Fri, 22 May 2026 18:38:03 UTC (12,369 KB)
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