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.
Vision-based bio-inspired control strategies offer great promise in demonstrating safe autonomous navigation of aerial microsystems in completely unstructured environments. This paper presents an innovative navigation technique that embeds bio-inspired widefield processing of instantaneous optic flow patterns within the H ∞ loop shaping synthesis framework, resulting in a dynamic controller that enables robust stabilization and command tracking behavior in obstacle-laden environments. The local environment is parameterized as a series of simpler corridor-like environments in the optic flow model, and the loop shaping controller is synthesized to provide robust stability across the range of modeled environments. Experimental validation is provided using a quadrotor aerial vehicle across environments with large variation in local structure, with the loop shaping controller demonstrating better tracking performance than other comparable controllers in straight-line corridors of different widths. The current approach is computationally efficient, as it does not involve explicit extraction of an environment depth map, and makes for an attractive paradigm for aerial microsystem navigation in urban environments. Corridor half-width, m Nomenclature INTRODUCTIONThe ability to sense and react to clutter within the payload and bandwidth constraints imposed by aerial microsystems is the primary requirement in demonstrating safe autonomous navigation in unstructured environments. This rules out the use of laser rangefinders and GPS-IMU based estimates of velocity and proximity to obstacles in the surrounding environment [1,2], as well as machine-vision based approaches that extract vehicle pose from camera imagery [3,4]. A safe control strategy usually relies on the synthesis of a flight controller that tracks the instantaneous obstacle-symmetric reference trajectory as the vehicle traverses the local environment. In unknown and unstructured environments, implementation becomes difficult requiring the resolution of the twin issues of local depth map extraction and accurate motion-state estimation at the bandwidths suitable for aerial navigation. Thus, most prior efforts have typically considered the simpler problem of navigation in known, well-structured environments [3][4][5][6][7][8][9][10][11][12], although onboard implementations of the coupled structure-motion state estimation problem in cluttered, urban environments exist [13,2].Bio-inspired vision-based control strategies offer great promise in demonstrating autonomous navigation in completely unstructured environments. Insects leverage information contained in optic flow, the patterns of visual motion that form on the retina as they move, by employing wide-field interneurons that transform large numbers of distributed, local estimates of optic flow into a reduced number of motor commands that stabilize and regulate flight behavior without explicit extraction of the local depth map in cluttered environments [14][15][16][17][18]. The resulting closed loop has b...
Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.
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