Robustness in bio‐inspired visually guided multi‐agent flight and the gain modulation hypothesis

Abstract

AbstractAerial swarm implementations often underperform their feedback design goals due to sensor noise, modeling errors, and external disturbances, each of which can often be mitigated by including robustness in the design approach. At the same time, gain modulation has been measured in numerous insect flight examples involving relative position regulation and tracking. Gains are often lower than what initially appears optimal, which is not fully explained by existing hypotheses. This article analyzes the robustness of a visual feedback control system modeling insect flight feedback paths by using optic flow to regulate an agent's motion relative to its neighbors. This study considers the system's robustness to sensor noise and configuration deviations, which appear as a nonlinearity in the observation equation often neglected in linear feedback. The analysis indicates robust performance can be attained by a static gain controller by appropriate choice of gain. Experimental results using an optic flow swarm implementation in robotic systems are used to quantify the actual system performance for static gain controllers. Performance is compared when the system responds to ideal optic flow (no sensor noise) and to camera‐based optic flow swarm implementation (sensor noise and configuration uncertainty). [Correction added on 10 November 2022, after first online publication: the underline in the word ‘compared’ of the preceding sentence has been removed in this version.] The analysis suggests neural gain modulation in insect feedback paths may support robust group motion stabilization.