Task partitioning in swarms of robots: an adaptive method for strategy selection
Task partitioning in swarms of robots: an adaptive method for strategy selection
复制标题
机器人群中的任务划分：策略选择的自适应方法
DOI：
10.1007/s11721-011-0060-1
复制
发表时间：
2011
影响因子：
2.6
通讯作者：
M. Birattari
中科院分区：
文献类型：
--
作者：
G. Pini;A. Brutschy;M. Frison;A. Roli;M. Dorigo;M. Birattari
任务划分是将一个任务分解为两个或多个可以单独处理的子任务。在许多群居昆虫物种中都可以观察到任务划分，因为它通常是组织一群个体工作的有利方式。任务划分的潜在优势包括：减少工人之间的干扰、利用个人的技能和专长、能源效率和更高的并行性。尽管蜂群机器人可以像群居昆虫一样从任务划分中受益，但只有很少的蜂群机器人研究致力于这一主题。在本文中，我们研究了一群机器人必须处理一个任务的情况，该任务可以分为两个子任务序列。我们提出了一种方法，允许群体中的单个机器人决定是否划分给定的任务。该方法是自组织的，依赖于每个个体的经验，并且不需要机器人之间的明确通信。以觅食为实验平台，在仿真实验中对该方法进行了验证。我们研究了任务划分更可取和不可取的情况。我们证明了所提出的方法在两种情况下都能带来良好的性能，只在有利的情况下使用任务划分。我们还表明，蜂群能够通过在线适应行为来对环境条件的变化做出反应。可扩展性实验表明，该方法在所有被测群体规模下都具有良好的性能。
Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals’ skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.