Autonomous task partitioning in robot foraging: an approach based on cost estimation
Autonomous task partitioning in robot foraging: an approach based on cost estimation
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DOI：
10.1177/1059712313484771
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发表时间：
2013-04-01
影响因子：
1.6
通讯作者：
Birattari, Mauro
中科院分区：
文献类型：
--
作者：
Pini, Giovanni;Brutschy, Arne;Birattari, Mauro
我们提出了一种在觅食机器人群体中进行自主任务划分的方法。任务划分是将任务分解为子任务的过程。任务划分会影响任务的执行和相关成本。我们的方法的特点是使用成本函数，将子任务的大小映射到总任务成本。机器人对成本函数进行建模，并使用该模型来选择要执行的子任务，目的是将成本降至最低。我们的方法将任务划分过程从特定于任务的操作中分离出来，并且它不需要对要采用的最佳划分策略进行先验假设。我们研究了一种觅食场景，其中对象运输由不同的机器人执行，每个机器人在有限的距离内移动对象。机器人根据我们的方法自主决定行驶的距离。机器人将里程计用于导航目的；我们证明了任务划分减少了里程计误差的影响并提高了性能。我们使用基于模拟的实验来验证我们的方法。我们研究了在不同的里程计精度水平、环境和蜂群的大小以及总的运输距离等不同的实验条件下，蜂群是如何划分运输的。我们的方法会产生适合每种情况的分区解决方案。
We propose an approach for autonomous task partitioning in swarms of foraging robots. Task partitioning is the process of decomposing tasks into sub-tasks. Task partitioning impacts tasks execution and associated costs. Our approach is characterized by the use of a cost function, mapping the size of sub-tasks to the overall task cost. The robots model the cost function and use the model to select sub-tasks to perform, aiming to minimize costs. Our approach separates the task partitioning process from task-specific actions and it does not require a priori assumptions to be made about the best partitioning strategy to employ. We study a foraging scenario in which object transportation is performed by different robots, each moving objects for a limited distance. The robots autonomously decide the distance traveled on the basis of our approach. The robots use odometry for navigational purposes; we show that task partitioning reduces the impact of odometry errors and improves performance. We validate our approach using simulation-based experiments. We study how the swarm partitions transportation under a number of experimental conditions characterized by different levels of odometry accuracy, size of the environment and the swarm, and total transportation distance. Our approach leads to partitioning solutions that are appropriate for each condition.