Multi-Robot Coordination in Complex Environment with Task and Communication Constraints
Multi-Robot Coordination in Complex Environment with Task and Communication Constraints
复制标题
具有任务和通信约束的复杂环境中的多机器人协调
DOI：
10.5772/54379
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发表时间：
2013-05
影响因子：
2.3
通讯作者：
Yao, Min
中科院分区：
文献类型：
--
作者：
Yang, Jianhua;Zheng, Yao;Wu, Zhaohui;Yao, Min
摘要在多机器人系统（MRS）中，由于固有的通信约束，在任务分配阶段任务可能无法分配给任何机器人。这种负面影响在具有时间限制的任务中变得更加严重。因此，我们提出了基于约束的方法（CoBA），一个基于市场的任务分配方法，使多机器人协调域之间的时间约束的子任务的一个复杂的任务和机器人之间的网络约束。我们通过让每个机器人维护它所知道的机器人的动态熟人网络来处理网络约束，并允许机器人在任务拍卖期间代表另一个机器人提交投标（“间接投标”）。为了对复杂任务进行建模，我们引入了带有时间约束的AND/OR任务树。提出了一种拍卖清算程序，它支持与/或任务树的时间约束和直接/间接的任务拍卖，使有效的多机器人任务分配，尽管各种限制。通过一系列灾害响应领域的实验，在仿真和物理环境中对该解决方案进行了验证。具体来说，我们研究了系统的性能分别通过改变机器人的数量，预期的任务发布率，通信可靠性因素，MRS的组成，以及熟人关系参数，在模拟实验。结果表明，我们的解决方案优于其他解决方案，即机器人能够更迅速有效地完成任务。
Abstract The tasks would fail to be assigned to any robots in the task allocation phase as a consequence of the inherent communication constraints in multi-robot systems (MRS). This negative effect becomes even more serious in tasks with temporal constraints. We therefore propose the constraint-based approach (CoBA), a market-based task allocation approach to enable multi-robot coordination in domains with temporal constraints between subtasks of a complex task and network constraints between robots. We handle network constraints by having each robot maintain a dynamic acquaintance network of robots that it knows about, and allowing a robot to submit a bid on behalf of another robot during a task auction (“indirect bidding”). In order to model the complex task, we introduce the AND/OR task tree with temporal constraints. An auction-clearing routine, which supports the AND/OR task tree with temporal constraints and direct/indirect task auction, is proposed to enable effective multi-robot task allocation in spite of various constraints. The solution was validated in both simulation and physical environments by a series of experiments in disaster response domains. Specifically, we study the system performance by separately varying the number of robots, the expected rate of task issuance, the communication reliability factor, the compositions of MRS, as well as the acquaintance relationship parameter, in simulation experiments. The results suggest that our solution outperforms others, that is, robots were able to complete the tasks more promptly and effectively.