# DD-GBP：通过分布式动态高斯信念传播实现随时多臂规划与连续执行

> 原文链接：https://robotics.hellobike.com/papers/2026-10-14-2610-20460
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：针对动态环境中长时程多臂在线安全执行，DD-GBP以分布式动态高斯信念传播规划关节轨迹，并在多臂场景和双臂运输实验中验证。

# DD-GBP：通过分布式动态高斯信念传播实现随时多臂规划与连续执行

::: v-pre
> 原文链接：[DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation](https://arxiv.org/abs/2610.20460)（arXiv 2610.20460 · 发布日期：2026-10-14）

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

针对动态环境中长时程多臂在线安全执行，DD-GBP以分布式动态高斯信念传播规划关节轨迹，并在多臂场景和双臂运输实验中验证。

## 核心要点

- 复杂动态环境中的长时程多臂任务，需要在线安全规划并连续执行，仍具挑战。
- DD-GBP通过并行局部推理规划关节空间轨迹，并以GP参数化连接规划时域、插值因子和执行。
- 各机器人维护本地树状因子图，异步交换消息；动态子网重建和事件触发更新降低开销。
- 在50个四臂动态障碍场景中，无碰撞全目标完成率92.5%，成功率高于六个代表性基线。

**验证范围与局限**：验证包括仿真的四臂动态障碍场景、30臂无障碍测试，以及动态干扰下的双臂运输实验。

## 英文摘要 (Abstract)

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.

作者：Zhao Jin, Fengyi Dai, Yuetong Yin, Yunan Wang, Wenjie Chen, Chuxiong Hu
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## 原始参考来源 (Sources)

- [arXiv 2610.20460：DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation](https://arxiv.org/abs/2610.20460)