Université Libre de Bruxelles.
Artif Life. 2014 Summer;20(3):291-317. doi: 10.1162/ARTL_a_00132. Epub 2014 Apr 14.
We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario.
我们在群体机器人的背景下研究任务划分。任务划分是将任务分解为可以由不同工人完成的子任务。我们专注于将任务划分为一系列必须按特定顺序执行的子任务的情况。这意味着子任务必须相互接口，并且一个子任务的输出被用作后续子任务的输入。可以区分具有直接传输和间接传输的任务划分。我们专注于第一种情况：子任务的输出直接从执行该子任务的个体传输到执行后续子任务的个体。作为我们研究的测试平台，我们使用执行觅食任务的机器人群体。机器人必须从位于未知位置的源中收获物体，并将它们运送到家的位置。当机器人找到源时，它会记住其位置并使用航位推算返回那里。在机器人学中，航位推算很有吸引力，因为它是一种廉价的定位方法，并且不需要任何额外的外部基础设施。但是，如果不定期校正，航位推算会导致随时间增加的误差。我们比较了一种不利用任务划分的觅食策略和一种利用任务划分的策略。我们表明，通过任务划分进行合作可以用来限制航位推算误差的影响。这导致定位物体源的能力提高和群体性能提高。我们使用实现的系统作为测试平台来研究具有直接传输的任务划分的收益和成本。我们使用真实机器人实现了该系统，在觅食场景中证明了我们方法的可行性。
