In this paper, we present a novel distributed task-allocation algorithm, namely the Sequential Task Addition Distributed Assignment Algorithm (STADAA), for autonomous multi-robot systems. The proposed STADAA can implemented in applications such as search and rescue, mobile-target tracking, and Intelligence, Surveillance, and Reconnaissance (ISR) missions. The proposed STADAA is developed by modifying an algorithm (i.e., the Task Oriented Distributed Assignment Algorithm (TODAA)) we previously developed based on the Hungarian algorithm. The STADAA employs a conflict-resolution mechanism that utilizes a slack variable, sequentially adding new admissible tasks to an admissible task list when there exists conflict in an assignment. The STADAA aims to minimize the resulting cost of the task assignments. We compare the STADAA with the Consensus-Based Auction Algorithm (CBAA), the Distributed Hungarian Based Algorithm (DHBA), and the TODAA in terms of the computational time, optimality, the number of steps to converge, and algorithmic complexity. The results show that the STADAA outperforms the CBAA and the TODAA in optimality and outperforms the DHBA in computational time.

在本文中，我们为自主多机器人系统提出了一种新颖的分布式任务分配算法，即顺序任务添加分布式分配算法（STADAA）。提议的 STADAA 可以在搜索和救援、移动目标跟踪以及情报、监视和侦察 (ISR) 任务等应用中实施。提议的 STADAA 是通过修改我们之前基于匈牙利算法开发的算法（即面向任务的分布式分配算法（TODAA））而开发的。STADAA 采用冲突解决机制，该机制利用松弛变量，当分配中存在冲突时，将新的可接受任务顺序添加到可接受任务列表中。STADAA 旨在最小化任务分配的成本。我们将 STADAA 与基于共识的拍卖算法 (CBAA)、基于分布式匈牙利的算法 (DHBA) 和 TODAA 在计算时间、最优性、收敛步数和算法复杂度方面进行了比较。结果表明，STADAA 在最优性上优于 CBAA 和 TODAA，在计算时间上优于 DHBA。