提出异步分布式轨迹估计算法，显著减少通信量并提升鲁棒性。
Technical Report: Asynchronous Distributed Trajectory Estimation of Multi-Robot Systems
- 采用异步块坐标下降法，降低通信开销
- 通信减少达96.9%，误差比现有算法低64%
- 对毫秒至秒级延迟均鲁棒，适合真实机器人系统
分布式轨迹估计在机器人领域广泛应用，但现有方法通常忽略代理间通信与计算的异步性。为此，本文提出一种异步块坐标下降算法用于分布式轨迹估计。考虑一群代理观测一组机器人，并在滑动窗口内估计其状态。代理求解最大后验估计问题的近似形式，该近似引入可忽略误差，且能消除高达96.9%的代理间通信。我们证明代理迭代值以指数速度收敛至最优状态估计。仿真显示该方法误差比同类先进算法最多低64%。移动机器人实验表明，该方法对跨度达三个数量级的延迟具有鲁棒性。
原文摘要 · Abstract (English)
Distributed trajectory estimation arises in many applications across robotics, but existing implementations typically do not consider asynchrony in agents' communications and computations. Therefore, we propose an asynchronous block coordinate descent algorithm for distributed trajectory estimation. We consider a team of agents that observes a team of robots and estimates the robots' states over a sliding window. The agents solve an approximation of the maximum a posteriori estimation problem, which we derive. We show this approximation introduces negligible errors and eliminates up to 96.9% of communications among agents. Next, we prove that agents' iterates converge exponentially fast to the optimal estimate of the robots' states. Simulations show that this approach has up to 64% less error than a comparable state-of-the-art algorithm. Experiments on mobile robots show this approach is robust to delays whose lengths span three orders of magnitude.
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