Role and task allocation framework for Multirobot collaboration with latent knowledge estimation

被引:2
作者
Gianni, Mario [1 ]
Uddin, Mohammad Salah [2 ]
机构
[1] Univ Plymouth, Sch Engn Comp & Math, Plymouth, Devon, England
[2] East West Univ, Comp Sci & Engn Dept, Dhaka 1212, Bangladesh
基金
英国工程与自然科学研究理事会;
关键词
knowledge discovery; knowledge factorization; knowledge prediction; multirobot collaboration; task allocation; URBAN SEARCH; SYSTEMS; REPRESENTATION; INFORMATION; EXCHANGE; SERVICE;
D O I
10.1002/eng2.12225
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi-Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV).
引用
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页数:27
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