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DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation — arXiv:2610.20460

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.20460
License (as archived): CC0-1.0
Archived: 2026-10-15T21:58:06.338Z
SHA-256 of the archived text: fc860b6572a2c3b531c12557c3d67115ad24fa59b4846db139a3140bf6ff71ab
Stable URL of this page: https://metraipolis.com/documento/dc_c7acd10dfd12

How to cite

DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation — arXiv:2610.20460 — arXiv (en), https://arxiv.org/abs/2610.20460 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c7acd10dfd12 (Metraipolis archive dc_c7acd10dfd12, sha256 fc860b6572a2c3b531c12557c3d67115ad24fa59b4846db139a3140bf6ff71ab)

BibTeX

@misc{dcc7acd10dfd12,
  title = {DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation — arXiv:2610.20460},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.20460},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c7acd10dfd12; sha256 fc860b6572a2c3b531c12557c3d67115ad24fa59b4846db139a3140bf6ff71ab},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.20460. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Safely executing long-horizon multi-arm tasks online in complex dynamic environments remains challenging. We propose a new distributed dynamic Gaussian belief propagation (DD-GBP) method that directly plans joint-space trajectories through parallel local inference. A unified Gaussian-process (GP) parameterization connects adaptive planning horizons, interpolation-time factors, and continuous execution. Each robot maintains a local tree-structured factor graph and exchanges messages asynchronously over active inter-arm links. Dynamic subnet reconstruction and event-triggered updates reduce communication and inference costs while responding to moving obstacles. We also provide theoretical analyses of local inference accuracy, anytime early stopping, triggered-replanning consistency, and GP-consistent execution. In 50 long-horizon, multi-target four-arm scenes with moving obstacles, DD-GBP achieves $92.5\%$ collision-free all-target completion and the highest success rate among six representative baselines. Scenario ablations demonstrate long-horizon safety and robustness to communication impairment and extreme conditions; success remains $86.0\%$ under $90\%$ packet loss. Execution results further show substantially suppressed extreme velocity and acceleration peaks relative to Syn-GBP. On one workstation, obstacle-free tests with 30 coexisting arms retain $99.87\%$ success and a $15.80$ ms mean warm solve, exceeding 60 Hz and outperforming the compared methods in scalability. Dual-arm transport experiments under dynamic interference demonstrate online response and practical deployability. To the best of our knowledge, these results establish a new state of the art for online long-horizon multi-arm planning in dynamic scenes.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.RO, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.20460 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T21:38:44.509Z dalla pagina https://arxiv.org/list/cs.RO/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).