Abstract
SummaryIn 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).
Highlights
Multirobot deployment is better suited than single robot deployment in many domain applications
We propose a framework for learning multidimensional relational structures (MDRSs) regulating role and task assignment in cooperative heterogeneous multirobot systems (CHMRSs)
We propose a generative approach based on a probabilistic model, similar to those applied for document classification and information retrieval.[56,57,58]
Summary
Multirobot deployment is better suited than single robot deployment in many domain applications. In safety-critical applications like Urban Search and Rescue, the deployment of multiple robots speeds up area coverage increasing the chance to find all potential survivors.[1] In rescue scenarios (considering the rescue environments after natural disaster such as earthquake, fire, building collapse etc.), it is very difficult for rescue workers or teams to access all the region of rescue environments due to the possible presence of radiation or extreme temperatures, dust, asbestos, hazardous substances.[2] A promising solution offered by rescue robots to assist rescue teams in terms of: Both authors were with Department of Computer Control and Management “A. Ruberti”, Sapienza University of Rome, Italy when the work was carried out. M. Gianni is with School of Engineering, Computing and Mathematics, University of Plymouth, UK, and M.S. Uddin is with Department of Computer Science and Engineering, East West University, Bangladesh
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