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IEEE Access

Date of Publication: 13 April 2020

https://doi.org/10.1109/ACCESS.2020.2987348

■  Researchers

Joonwon Choi¹, Yeongho Song¹, Seunghan Lim², Cheolhyeon Kwon¹, Hyondong Oh¹

¹  Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea

²  Agency for Defense Development (ADD), Daejeon, South Korea

■  Abstract

This paper proposes a formation control algorithm to create separated multiple formations for an undirected networked multi-agent system while preserving the network connectivity and avoiding collision among agents. Through the modified multi-consensus technique, the proposed algorithm can simultaneously divide a group of multiple agents into any arbitrary number of desired formations in a decentralized manner. Furthermore, the agents assigned to each formation group can be easily reallocated to other formation groups without network topological constraints as long as the entire network is initially connected; an operator can freely partition agents even if there is no spanning tree within each subgroup. Besides, the system can avoid collision without loosing the connectivity even during the transient period of formation by applying the existing potential function based on the network connectivity estimation. If the estimation is correct, the potential function not only guarantees the connectivity maintenance but also allows some extra edges to be broken if the network remains connected. Numerical simulations are performed to verify the feasibility and performance of the proposed multi-subgroup formation control.

■  Conclusion

This paper has proposed the algorithm that can divide mobile agents into multiple formations without collision and network connectivity lost. By combining modified Multi-Consensus approach with the potential function, the MAS can achieve multi-formation mission without needing initial joint connection between same subgroup members. As future work, the dynamic consensus will be addressed; The proposed algorithm only presents the stationary consensus, i.e., every formations will stop after they converge. Due to this limitation, the algorithm is hard to be applied to non-holonomic vehicles. Another topic is the optimization for subgroup allocation. The optimal allocation for the maximum efficiency might be varied depending on the environments and mission requirements. Moreover, allocating the optimal subgroup per each agent in a decentralized system is challenging. This issue could be solved by the decentralized optimal allocation algorithm.

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