AI 中文总结
针对多机器人系统中通信和计算异步的问题，提出异步块坐标下降算法，通过近似最大后验估计减少96.9%通信，证明指数收敛，误差比现有算法低64%。
AI 中文摘要
分布式轨迹估计在机器人学的许多应用中都有出现，但现有的实现通常不考虑智能体通信和计算的异步性。因此，我们提出了一种用于分布式轨迹估计的异步块坐标下降算法。我们考虑一组智能体观察一组机器人，并在滑动窗口上估计它们的状态。智能体求解我们推导出的最大后验估计问题的近似解。我们证明这种近似引入了可忽略的误差，并消除了智能体之间高达96.9%的通信。接下来，我们证明智能体的迭代以指数速度收敛到机器人状态的最优估计。仿真表明，与同类最先进算法相比，该方法的误差降低了高达64%。在移动机器人上的实验显示了该方法对延迟长度跨越三个数量级的鲁棒性。
英文摘要
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.
Comments13 pages, 3 figures
