Optimized Stochastic Policies for Task Allocation in Swarms of Robots
Optimized Stochastic Policies for Task Allocation in Swarms of Robots
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DOI：
10.1109/tro.2009.2024997
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发表时间：
2009-08-01
影响因子：
7.8
通讯作者：
Kumar, Vijay
中科院分区：
文献类型：
--
作者：
Berman, Spring;Halasz, Adam;Kumar, Vijay
我们提出了一种可扩展的方法来动态分配一群同构机器人到多个任务，这些任务将按照期望的分布并行执行。我们采用了一种分散的策略，不需要机器人之间的交流。它是基于对群体的连续抽象的发展，通过对群体分数进行建模，并将任务分配问题定义为机器人进入和退出每个任务的比率的选择。这些速率被用来确定为单个机器人定义随机控制策略的概率，这些随机控制策略反过来又产生期望的集体行为。我们解决了计算速率的问题，以实现群体在平衡状态下在任务之间切换的约束下的快速再分配。我们提出了该优化问题的几种公式，这些公式在任务之间的优先约束和它们对初始机器人分布的依赖方面有所不同。我们使用每个公式来优化具有四个任务的场景的速率，并使用250个机器人在四座建筑物中重新分配自己以调查周边的模拟来比较产生的控制策略。
We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.