This paper presents a consensus-based payload algorithm (CBPA) to deal with the condition of robots' capability decrease for multi-robot task allocation. During the execution of complex tasks, robots' capabilities could decrease with the consumption of payloads, which causes a problem that the robot coalition would not meet the tasks' requirements in real time. The proposed CBPA is an enhanced version of the consensus-based bundle algorithm (CBBA) and comprises two primary core phases: the payload bundle construction and consensus phases. In the payload bundle construction phase, CBPA introduces a payload assignment matrix to track the payloads carried by the robots and the demands of multi-robot tasks in real time. Then, robots share their respective payload assignment matrix in the consensus phase. These two phases are iterated to dynamically adjust the number of robots performing multi-robot tasks and the number of tasks each robot performs and obtain conflict-free results to ensure that the robot coalition meets the demand and completes all tasks as quickly as possible. Physical experiment shows that CBPA is appropriate in complex and dynamic scenarios where robots need to collaborate and task requirements are tightly coupled to the robots' payloads. Numerical experiments show that CBPA has higher total task gains than CBBA.

翻译：本文提出一种基于共识的有效载荷算法（CBPA），用于处理多机器人任务分配中机器人能力下降的情况。在执行复杂任务过程中，机器人的能力可能随着有效载荷的消耗而降低，这会导致机器人联盟无法实时满足任务需求。所提出的CBPA是基于共识的捆绑算法（CBBA）的增强版本，包含两个主要核心阶段：有效载荷捆绑构建阶段和共识阶段。在有效载荷捆绑构建阶段，CBPA引入有效载荷分配矩阵来实时跟踪机器人携带的有效载荷以及多机器人任务的需求。随后，机器人在共识阶段共享各自的有效载荷分配矩阵。这两个阶段迭代进行，以动态调整执行多机器人任务的机器人数量以及每个机器人执行的任务数量，并获得无冲突的结果，从而确保机器人联盟满足需求并尽可能快地完成所有任务。物理实验表明，CBPA适用于机器人需要协作且任务需求与机器人有效载荷紧密耦合的复杂动态场景。数值实验表明，CBPA相比CBBA具有更高的总任务收益。