Rapid Information Transfer in Swarms under Update-Rate-Bounds using Delayed Self Reinforcement

[+] Author and Article Information
Santosh Devasia

Professor, Fellow of ASME, Mechanical Engineering Department, U. of Washington, Seattle, Washington 98195-2600

1Corresponding author.

ASME doi:10.1115/1.4042949 History: Received April 13, 2018; Revised February 15, 2019


The effectiveness of a network's response to external stimuli depends on rapid distortion-free information transfer across the network. However, the rate of information transfer, when each agent aligns with information from its network neighbors, is limited by the update rate at which each individual can sense and process information. Moreover, such neighbor-based, diffusion-type information transfer does not predict the superfluid-like information transfer during swarming maneuvers observed in nature. The main contribution of this article is to propose a novel model that uses self reinforcement, where each individual augments its neighbor-averaged information update using its previous update, to (i) increase the information-transfer rate without requiring an increased, individual update-rate; and (ii) enable superfluid-like information transfer. Simulations results of example systems show substantial improvement, more than an order of magnitude increase, in the information transfer rate, without the need to increase the update rate. Moreover, results show that the DSR approach's ability to enable superfluid-like, distortion-free information transfer results in maneuvers with smaller turn radius and improved cohesiveness. Such faster response rate with limited individual update rate can enable better understanding of cohesiveness of flocking in nature, as well as improve the performance of engineered swarms such as unmanned mobile systems.

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