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

Abstract

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).

Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines

“…Using multiple UAVs which hover close to each other during a maintenance operation, a task can be accomplished relatively fast by quickly taking turns to perform different roles such as drilling [71], cleaning [72], coating [73], or painting [74,75]. A careful sequence of operations can be planned for multiple UAVs or for multi-robot [76] performing such operations by modelling roles and task allocation within a cooperative heterogeneous system. Based on many-sorted first-order logic, assigning roles and tasks for heterogeneous multi-robot operations can be achieved using temporal relations.…”