Complete coverage, which is integral to many robotic applications, aims to cover an area as quickly as possible. In such tasks, employing multiple robots can reduce the overall coverage time by appropriate task allocation. Several multi-robot coverage approaches divide the environment into balanced subareas and minimize the maximum subarea of all robots. However, balanced coverage in many situations, such as in the cases of robots with different velocities and heterogeneous multi-robot systems, may have inefficient results. This study addresses the unbalanced complete coverage problem of multiple robots with different velocities for a known environment. First, we propose a novel credit model to transform the unbalanced coverage problem into a set of single-objective optimization problems, which can find a combinational optimal solution by optimizing each separate objective function of the single-objective optimization problem to alleviate the computational complexity. Then, we propose a credit-based algorithm composed of a cyclic region growth algorithm and a region fine-tuning algorithm. The cyclic region growth algorithm finds an initial solution to the single-objective optimization problems set by a regional growth strategy with multiple restricts, whereas the region fine-tuning algorithm reallocates the tasks of the partitions with too many tasks to the partitions with too few tasks by constructing a search tree, thereby converging the initial solution to the optimal solution. Simulation results indicate that compared with conventional multi-robot complete coverage problem algorithms, the credit-based algorithm can obtain the optimal solution with the increased number of robots and enlarged size of the mission environment.

完全覆盖是许多机器人应用程序不可或缺的一部分，旨在尽快覆盖一个区域。在此类任务中，使用多个机器人可以通过适当的任务分配来减少整体覆盖时间。几种多机器人覆盖方法将环境划分为平衡的子区域，并最小化所有机器人的最大子区域。然而，在许多情况下，例如在具有不同速度的机器人和异构多机器人系统的情况下，平衡覆盖可能会产生低效的结果。本研究解决了在已知环境中具有不同速度的多个机器人的不平衡完全覆盖问题。首先，我们提出了一种新颖的信用模型，将不平衡覆盖问题转化为一组单目标优化问题，它可以通过优化单目标优化问题的每个单独的目标函数来找到组合最优解，以减轻计算复杂度。然后，我们提出了一种由循环区域增长算法和区域微调算法组成的基于信用的算法。循环区域增长算法为具有多个限制的区域增长策略设置的单目标优化问题找到初始解决方案，而区域微调算法将任务过多的分区的任务重新分配给任务过少的分区通过构建搜索树，从而将初始解收敛到最优解。仿真结果表明，与传统的多机器人完全覆盖问题算法相比，