Abstract
This work deals with the development of a dynamic task assignment strategy for heterogeneous multi-robot teams in typical real world scenarios. The strategy must be efficiently scalable to support problems of increasing complexity with minimum designer intervention. To this end, we have selected a very simple auction-based strategy, which has been implemented and analysed in a multi-robot cleaning problem that requires strong coordination and dynamic complex subtask organization. We will show that the selection of a simple auction strategy provides a linear computational cost increase with the number of robots that make up the team and allows the solving of highly complex assignment problems in dynamic conditions by means of a hierarchical sub-auction policy. To coordinate and control the team, a layered behaviour-based architecture has been applied that allows the reusing of the auction-based strategy to achieve different coordination levels.
Highlights
Facing real‐world problems like multi‐target observation, exploration or collective object transportation with a team of autonomous robots implies taking into consideration two very relevant aspects
The remainder of the paper is structured as follows: section 2 contains the formal description of the behaviour‐based architecture that provides the controllers of the robots that make up the MRS and the description of the specific auction‐based task assignment strategy that has been implemented
The problem selected as a testing platform is a collective cleaning task. This type of problem is a typical benchmark [1] in strongly coordinated MRSs because it contains all the elements of a real‐world problem suitable for them: due to cost, the robots that make up the team are usually specialized in different aspects of cleaning, it requires strong synchronization to be efficiently performed, the sub‐task assignment is highly dynamic because the robots must be reassigned to new areas continuously, the complexity of the task can be very high depending upon the physical features of the zone to be cleaned, and so on
Summary
Facing real‐world problems like multi‐target observation (surveillance), exploration or collective object transportation with a team of autonomous robots implies taking into consideration two very relevant aspects. We will show how the computational cost increases linearly with the number of robots in the team in simple cases and how, through a hierarchical sub‐auction policy, highly complex assignment problems can be solved while maintaining the linear computational cost increase This last analysis is the main contribution of this work, as the automatic creation and management of sub‐auctions in real cases has rarely been addressed within the field [14]. The remainder of the paper is structured as follows: section 2 contains the formal description of the behaviour‐based architecture that provides the controllers of the robots that make up the MRS and the description of the specific auction‐based task assignment strategy that has been implemented.
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