Distributed planning method enables safe multi-arm robot tasks online
DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation
Robotics
Summary
Planning safe movements for multiple robot arms working together in busy spaces is really hard. The authors created a new way for each robot arm to plan its path while sharing only important information with others, helping all arms move smoothly and avoid crashes. Their method works quickly, even when many arms are working or communication is unreliable, and keeps robots moving safely around moving obstacles. Tests show this approach is better than others for long tasks where arms must move carefully and respond to changes.
What this means in practice
- •For robotic manufacturing teams: Coordinate multiple robot arms to perform complex assembly tasks safely and efficiently while adapting to moving obstacles and communication delays.
- •For warehouse automation operators: Deploy multi-arm robotic systems that can handle long-duration pick-and-place jobs online with safety against dynamic changes in the environment.
Authors
Zhao Jin, Fengyi Dai, Yuetong Yin, Yunan Wang, Wenjie Chen, Chuxiong Hu
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.