Exerting human control over decentralized robot swarms
Exerting human control over decentralized robot swarms
复制标题
对分散的机器人群进行人类控制
DOI：
--
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
M. A. Potter
中科院分区：
文献类型：
--
作者：
Z. Kira;M. A. Potter
机器人群仅使用代理之间的本地交互就能够以健壮性和灵活性执行任务。这样的系统可能会导致通常需要的紧急行为，但在设计后很难控制和操纵。这些特性使得人类操作员对蜂群的实时控制具有挑战性--这是一个在文献中没有得到充分解决的问题。在本文中，我们提出了两种可能的控制形式：自上而下的全局群特征控制和通过影响群成员的子集进行自下而上的控制。我们提出了解决这些问题的学习方法。第一种方法使用基于实例的学习来从特定情况下的参数空间和全局特征的采样中产生广义模型。第二种方法使用进化学习来学习影响群中机器人的虚拟智能体的放置和参数化。最后，我们展示了这些方法是如何推广的，并且可以被人类操作员用来实时动态地控制群。
Robot swarms are capable of performing tasks with robustness and flexibility using only local interactions between the agents. Such a system can lead to emergent behavior that is often desirable, but difficult to control and manipulate post-design. These properties make the real-time control of swarms by a human operator challenging—a problem that has not been adequately addressed in the literature. In this paper we present preliminary work on two possible forms of control: top-down control of global swarm characteristics and bottom-up control by influencing a subset of the swarm members. We present learning methods to address each of these. The first method uses instance-based learning to produce a generalized model from a sampling of the parameter space and global characteristics for specific situations. The second method uses evolutionary learning to learn placement and parameterization of virtual agents that can influence the robots in the swarm. Finally we show how these methods generalize and can be used by a human operator to dynamically control a swarm in real time.