动态分配多机器人任务，实时应对负载消耗导致的能力下降。
Consensus-Based Dynamic Task Allocation for Multi-Robot System Considering Payloads Consumption
- 基于共识的负载分配算法，实时追踪机器人负载与任务需求。
- 相比CBBA，总任务收益提升，且能动态调整机器人协作数量。
- 适合负载敏感、任务耦合紧密的复杂动态场景，如救援或运输。
本文提出一种基于共识的负载算法（CBPA），用于解决多机器人系统在执行复杂任务时因负载消耗导致能力下降的问题。随着任务进行，机器人负载消耗会降低其能力，使得原有机器人组合无法满足任务需求。提出的CBPA是共识基捆绑算法（CBBA）的改进版，包含两个核心阶段：负载捆绑构建和共识阶段。在负载捆绑构建阶段，引入负载分配矩阵实时追踪各机器人携带的负载及多机器人任务的需求。随后，在共识阶段，机器人共享各自的负载分配矩阵。通过两阶段迭代，动态调整参与任务的机器人数量及每台机器人承担的任务数，实现无冲突分配，确保机器人团体满足需求并尽可能快速完成所有任务。物理实验表明，该算法适用于需要协作且任务要求高度依赖负载的复杂动态场景。数值实验显示，相较于CBBA，CBPA具有更高的总任务收益。
原文摘要 · Abstract (English)
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.
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