Multi-Robot Coordination for Estimation and Coverage of Unknown Spatial Fields
Multi-Robot Coordination for Estimation and Coverage of Unknown Spatial Fields
复制标题
多机器人协调未知空间场的估计和覆盖
DOI：
10.1109/icra40945.2020.9197487
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Egerstedt
中科院分区：
文献类型：
--
作者：
Alessia Benevento;María Santos;G. Notarstefano;K. Paynabar;M. Bloch;M. Egerstedt
我们提出了一种用于多机器人覆盖以密度函数为特征的初始未知空间标量场的算法，其中一组机器人同时估计和优化其在域上的密度函数的覆盖范围。所提出的算法借用了贝叶斯优化与高斯过程的强大概念，当与控制律相结合以实现质心 Voronoi 细分时，产生了一种自适应顺序采样方法来探索和覆盖域。该方法的关键是使用真实密度函数的代理函数来应用控制律，然后随着机器人收集更多样本进行估计而不断完善该控制律。通过证明相对于已知密度函数获得的覆盖范围的渐近无遗憾，该算法的性能在稍微理想化的假设下在理论上得到了证明。性能还在模拟中和机器人小团队的机器人馆中进行了评估，证实了理论分析所建议的良好性能。
We present an algorithm for multi-robot coverage of an initially unknown spatial scalar field characterized by a density function, whereby a team of robots simultaneously estimates and optimizes its coverage of the density function over the domain. The proposed algorithm borrows powerful concepts from Bayesian Optimization with Gaussian Processes that, when combined with control laws to achieve centroidal Voronoi tessellation, give rise to an adaptive sequential sampling method to explore and cover the domain. The crux of the approach is to apply a control law using a surrogate function of the true density function, which is then successively refined as robots gather more samples for estimation. The performance of the algorithm is justified theoretically under slightly idealized assumptions, by demonstrating asymptotic no-regret with respect to the coverage obtained with a known density function. The performance is also evaluated in simulation and on the Robotarium with small teams of robots, confirming the good performance suggested by the theoretical analysis.