Detecting intermittent switching leadership in coupled dynamical systems

Leader-follower relationships are commonly hypothesized as a fundamental mechanism underlying collective behaviour in many biological and physical systems. Understanding the emergence of such behaviour is relevant in science and engineering to control the dynamics of complex systems toward a desired state. In prior works, due in part to the limitations of existing methods for dissecting intermittent causal relationships, leadership is assumed to be consistent in time and space. This assumption has been contradicted by recent progress in the study of animal behaviour. In this work, we leverage information theory and time series analysis to propose a novel and simple method for dissecting changes in causal influence. Our approach computes the cumulative influence function of a given individual on the rest of the group in consecutive time intervals and identify change in the monotonicity of the function as a change in its leadership status. We demonstrate the effectiveness of our approach to dissect potential changes in leadership on self-propelled particles where the emergence of leader-follower relationship can be controlled and on tandem flights of birds recorded in their natural environment. Our method is expected to provide a novel methodological tool to further our understanding of collective behaviour

SURFACE: Swarming USVs and Resilient Formations Against Contested Environments

A Quad, describing CRUSER Seed Research Program funded research.CRUSER Funded ResearchFY22 Funded Research ProposalConsortium for Robotics and Unmanned Systems Education and Research (CRUSER

Group coordination in a biologically-inspired vectorial network model

Most of the mathematical models of collective behavior describe uncertainty in individual decision making through additive uniform noise. However, recent data driven studies on animal locomotion indicate that a number of animal species may be better represented by more complex forms of noise. For example, the popular zebrafish model organism has been found to exhibit a burst-and-coast swimming style with occasional fast and large changes of direction. Based on these observations, the turn rate of this small fish has been modeled as a mean reverting stochastic process with jumps. Here, we consider a new model for collective behavior inspired by the zebrafish animal model. In the vicinity of the synchronized state and for small noise intensity, we establish a closed-form expression for the group polarization and through extensive numerical simulations we validate our findings. These results are expected to aid in the analysis of zebrafish locomotion and contribute a new set of mathematical tools to study collective behavior of networked noisy dynamical systems

Analysis of Pairwise Interactions in a Maximum Likelihood Sense to Identify Leaders in a Group

Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader–follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader–follower relationships

Assessing the Predictive Performance of Two DNN Models: A Comparative Analysis to Support Reusing Training Weights for Autonomous Aerial Refueling Missions

The United States Navy aims to enhance its fleet by expanding the deployment of unmanned aircraft in carrier air wings. However, certifying the autonomous refueling of unmanned aerial platforms currently lacks a publicly available method. Ongoing research at the United States Naval Academy focuses on investigating certification evidence that would enable a deep neural network (DNN) to facilitate autonomous aerial refueling (AAR). This study explores training a DNN to accurately detect the drogue and coupler deployed by a KC-130 tanker and a tanker-configured F/A-18 jet. Both tankers have a similar drogue refueling system but differ vastly in image background noise and contrast, posing a challenge for object detection. Using salient metrics, the performance of a DNN model trained separately on video footage of both tankers is tested to enable the AAR task. Our results indicate that a DNN trained on developmental flight test videos of aircraft refueling from a KC-130 tanker effectively completes the aerial refueling task on a F/A-18 tanker compared to another DNN trained on video footage of the same tanker. These findings might validate the idea that a DNN trained on a specific aircraft dataset with a similar probe and drogue refueling system satisfactorily performs the aerial refueling task on various tankers, eliminating the need for additional training data for each tanker individually

Criteria for stochastic pinning control of networks of chaotic maps

This paper investigates the controllability of discrete-time networks of coupled chaotic maps through stochastic pinning. In this control scheme, the network dynamics are steered towards a desired trajectory through a feedback control input that is applied stochastically to the network nodes. The network controllability is studied by analyzing the local mean square stability of the error dynamics with respect to the desired trajectory. Through the analysis of the spectral properties of salient matrices, a toolbox of conditions for controllability are obtained, in terms of the dynamics of the individual maps, algebraic properties of the network, and the probability distribution of the pinning control. We demonstrate the use of these conditions in the design of a stochastic pinning control strategy for networks of Chirikov standard maps. To elucidate the applicability of the approach, we consider different network topologies and compare five different stochastic pinning strategies through extensive numerical simulations

Zebrafish swimming in the flow: a particle image velocimetry study

Zebrafish is emerging as a species of choice for the study of a number of biomechanics problems, including balance development, schooling, and neuromuscular transmission. The precise quantification of the flow physics around swimming zebrafish is critical toward a mechanistic understanding of the complex swimming style of this fresh-water species. Although previous studies have elucidated the vortical structures in the wake of zebrafish swimming in placid water, the flow physics of zebrafish swimming against a water current remains unexplored. In an effort to illuminate zebrafish swimming in a dynamic environment reminiscent of its natural habitat, we experimentally investigated the locomotion and hydrodynamics of a single zebrafish swimming in a miniature water tunnel using particle image velocimetry. Our results on zebrafish locomotion detail the role of flow speed on tail beat undulations, heading direction, and swimming speed. Our findings on zebrafish hydrodynamics offer a precise quantification of vortex shedding during zebrafish swimming and demonstrate that locomotory patterns play a central role on the flow physics. This knowledge may help clarify the evolutionary advantage of burst and cruise swimming movements in zebrafish