Method for decomposition a global task into local tasks through the process of collective decision making in swarm robotic systems

Abstract

Objective. The purpose of the work is to increase the efficiency of the process of task allocation through preliminary decomposition and prioritization of tasks based on the process of collective decision-making (CDM) in swarm robotic systems (SRS).Method. Task decomposition implemented using a hybrid decision-making strategy, the majority principle of changing opinions using distributed registry technology for disseminating opinions among SRS agents and aggregating accumulated knowledge about the functioning environment. An element of scientific novelty is the proposed SRS procedure, which provides the possibility of assessing the priority of tasks, which in turn improves the efficiency of the SRS agents.Result. Software implementation of the proposed method made in the C++ programming language. For the experiments, the scenario of collective perception by SRS agents in a specialized simulation environment ARGoS was used.Conclusion. The method implemented using the solutions proposed in the work turned out to be more efficient than the method based on the greedy algorithm. The proposed solutions can be used not only in SRS, but also in any other robotic systems with decentralized control, designed to monitor and control environmental parameters.