Resource match cost based multi-UAV decentralized coalition formation in an unknown region
Syed Arsalan Ali, Gao Xiaoguang, Xiaowei Fu · 2017
This paper proposes an algorithm for the decentralized coalition formation of multiple heterogeneous UAVs that cooperatively perform a search and attack mission to neutralize the static or dynamic ground targets in a highly uncertain region where no prior information is available about the targets, and no centralized communication link with the UAVs is possible. In such cooperative missions, if the detecting UAV does not have enough resources to neutralize a target then a coalition of UAVs may needs to be formed that fulfills the target resource requirement. This coalition formation is computationally complex due to the combinatorial nature of the problem and is NP-Hard. Therefore, solutions with low computational complexity are required. The proposed decentralized coalition formation algorithm is sub-optimal and is computationally less complex. The proposed algorithm is based on the resource match cost criteria, in which the algorithm iteratively selects the final coalition members from the responding UAVs which are closest in resource match to the target resource requirement. After the selection of every single coalition member, the new resource match cost on the basis of new target resource requirement is calculated for the remaining responding UAVs and the above procedure is repeated until the target resource requirement is satisfied. The main objective of the algorithm is that, the coalition formed must be of minimum size and closest in match to the target resource requirement, so that more UAVs and resources must remain available for the search of other targets. The proposed solution enables the UAVs to form parallel coalitions to neutralize multiple targets and to utilize their resources more effectively. The performance of the proposed algorithm is evaluated through simulation tests and the results are compared with one of the reference sub-optimal decentralized coalition formation algorithm. The results show that the proposed algorithm is more effective and determines the near optimal set of member UAVs for the decentralized coalition formation. However, the proposed algorithm is naive with less computational complexity and can produce sub-optimal solutions which in most cases are near optimal.