Research on task allocation for multi-type task of unmanned surface vehicles

Unmanned surface vehicles (USVs) are gaining significant interest, particularly in support of complex maritime operations. To ensure efficient autonomous operation of the USV swarm in the case of multi-type missions, a task allocation system that combines off-line and on-line task allocation is constructed in this paper. The task set of each USV is obtained by improved genetic algorithm (GA). A distance matrix of these tasks is calculated by fast marching method (FMM) and integrated into the self-attention mechanism (SAM). The algorithm can shorten swarm's total voyage and make USVs' task load balanced. Furthermore, to accommodate the situations of task addition, deletion, and failure of USVs, an on-line task reallocation algorithm called the improved contract net protocol and vacancy chain (ICNP-VC) is developed. It unites the contract net protocol (CNP) and vacancy chain (VC) to enhance the intelligence of USVs. The ICNP-VC modifies the content of the bid and distinguishes the type of the cost. Meanwhile, the algorithm can balance the task load of the cluster and reduces the waste of resources. Through extensive simulations, demonstrate the proposed algorithms' capability to optimize task allocation and plan safe paths, leading to improved performance in complex marine scenarios.