This paper presents an adaptive online distributed optimal control approach that is applicable to optimal planning for very-large-scale robotics systems in highly uncertain environments. This approach is developed based on the optimal mass transport theory. It is also viewed as an online reinforcement learning and approximate dynamic programming approach in the Wasserstein-GMM space, where a novel value functional is defined based on the probability density functions of robots and the time-varying obstacle map functions describing the changing environmental information. The proposed approach is demonstrated on the path planning problem of very-largescale robotic systems where the approximated layout of obstacles in the workspace is incrementally updated by the observations of robots, and compared with some existing state-of-the-art approaches. The numerical simulation results show that the proposed approach outperforms these approaches in aspects of the average traveling distance and the energy cost.
在协作机器人的背景下，分布式态势感知对于支持机器人和人类代理团队中的集体智能至关重要，它可以用于个人和集体决策支持。这在与紧急救援和危机管理有关的应用中尤其重要。在操作任务期间，数据和知识由异构机器人和人类以不同的方式逐步收集。我们将其描述为\emph{Hastily Formed Knowledge Networks}（HFKNs）的创建。本文的重点是一个通用的分布式系统架构，支持创建HFKNs的机器人和人类的团队的规范和原型。收集的信息范围从低级传感器数据到高级语义知识，后者部分表示为RDF图。该框架包括一个同步协议和相关的算法，允许代理之间的数据和知识的自动分配和共享。这是通过代理之间共享的RDF图的分布式同步来完成的。SPARQL中指定的高级语义查询可以被机器人和人类用来从团队成员那里获取知识和数据内容。该系统的经验验证和所提出的算法的复杂性结果。此外，现场机器人的案例研究进行了描述，在一个合作的紧急救援情况下，同时使用完整的HFKN框架，使用几个无人机执行3D映射任务。
Distributed Adaptive Control: An ideal Cognitive Architecture candidate for managing a robotic recycling plant
Institute for Bioengineering of Catalonia (IBEC)（加泰罗尼亚生物工程研究所）；Barcelona Institute of Science and Technology (BIST)（巴塞罗那科学技术研究院）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚高级研究学院）
