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A team-based approach for coverage control of moving sensor networks. (English) Zbl 1372.93008
Summary: The present paper proposes a new team-based approach that allows for forming multiple teams of agents within the coverage control framework. The objective function defined for this purpose tends to minimize the accumulative distance from each agent while reckoning with the given density function that defines the probability of events in the environment to be covered. The proposed team-based approach via the defined optimization problem allows for forming teams of agents when for a variety of reasons, e.g., heterogeneity in their embedded communication capabilities or the dynamics, it is required to keep the similar agents together in the same team. To realize this, the overall objective function is defined as the accumulated sensing cost of individual agents belonging to different teams. The defined collective cost function captures the interdependency of the team’s Voronoi cells on the position of the agents that can be viewed as the impact of the dynamic boundaries on the agents. A gradient descent-based controller is designed to ensure the locally optimum configuration of the teams and agents within each team. The convergence of the proposed method is studied to ensure the stability of the implemented controller in both teams and agents final configuration. In addition, a new formation control approach is proposed using the team-based framework to impose either the same or different formation structures while performing the underlying coverage task. It is shown that maintaining the desired configuration through the proposed formation control is achieved at the cost of sacrificing the sensing performance. Finally, the proposed coverage and formation methods are examined via a numerical example where multiple heterogeneous teams of agents with potentially different number of agents are deployed.

MSC:
93A14 Decentralized systems
68T42 Agent technology and artificial intelligence
93C95 Application models in control theory
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