Rapid Information Transfer in Swarms Under Update-Rate-Bounds Using Delayed Self-Reinforcement

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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 paper 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, the results show that the delayed self-reinforcement (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.