控制趋光性和紧急集体行为的工程感觉延迟

M. Mijalkov, Austin Mcdaniel, J. Wehr, G. Volpe
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引用次数: 70

摘要

从自主移动个体的相互作用中产生的集体运动在许多现象中起着关键作用,从细菌菌落的生长到机器人群体的协调。为了使这些集体行为得以实现,个体必须能够发出、感知信号并对信号做出反应。在处理简单的生物体和机器人时,这些信号必然是非常基本的,例如,细胞可能通过释放化学物质来发出其存在的信号,而机器人可能通过发光来发出信号。一个额外的挑战出现了,因为个体的运动经常是嘈杂的,例如细胞的方向可以被布朗运动改变,机器人的方向可以被不平坦的地形改变。因此,重点是通过简单的自主代理以健壮的方式相互通信来实现复杂和可调的行为。在这里,我们展示了感知和响应信号之间的延迟可以决定运动本质上有噪声的自主代理的个人和集体长期行为。我们通过实验证明,一组能够发射径向衰减光场的光致机器人的集体行为可以通过控制它们根据测量的光强改变推进速度的延迟来从分离调整到聚集和聚类。我们将这种转变跟踪到该系统的潜在动力学,特别是机器人的感官延迟时间与机器人随机重新定向的特征时间之间的比率。在数字的支持下,我们讨论了如何将相同的机制应用于控制主动代理,例如空中无人机,在三维空间中移动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Engineering sensorial delay to control phototaxis and emergent collective behaviors
Collective motions emerging from the interaction of autonomous mobile individuals play a key role in many phenomena, from the growth of bacterial colonies to the coordination of robotic swarms. For these collective behaviours to take hold, the individuals must be able to emit, sense and react to signals. When dealing with simple organisms and robots, these signals are necessarily very elementary, e.g. a cell might signal its presence by releasing chemicals and a robot by shining light. An additional challenge arises because the motion of the individuals is often noisy, e.g. the orientation of cells can be altered by Brownian motion and that of robots by an uneven terrain. Therefore, the emphasis is on achieving complex and tunable behaviors from simple autonomous agents communicating with each other in robust ways. Here, we show that the delay between sensing and reacting to a signal can determine the individual and collective long-term behavior of autonomous agents whose motion is intrinsically noisy. We experimentally demonstrate that the collective behaviour of a group of phototactic robots capable of emitting a radially decaying light field can be tuned from segregation to aggregation and clustering by controlling the delay with which they change their propulsion speed in response to the light intensity they measure. We track this transition to the underlying dynamics of this system, in particular, to the ratio between the robots' sensorial delay time and the characteristic time of the robots' random reorientation. Supported by numerics, we discuss how the same mechanism can be applied to control active agents, e.g. airborne drones, moving in a three-dimensional space.
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