Swarm shepherding using bearing-only measurements

Drawing inspiration from natural herding behaviours, shepherding provides a method for swarm guidance that utilizes steering agents and can be applied in biological and robotics systems at various scales. However, while most shepherding research has relied on the precise sensing capabilities of steering agents, these assumptions do not necessarily hold in real-world tasks. To fill in the gap between practice and literature, in this study, we demonstrate that swarm shepherding can be achieved via bearing-only measurements, and explore the minimum amount of information required. We initially formulate our algorithm for a single agent and subsequently expand its application to accommodate multiple agents, incorporating strategies tailored for herding multiple swarms. Numerical simulations show the effectiveness of the algorithm under various initial placements and configurations. The minimum amount of information required by the proposed algorithm for successful shepherding, i.e. a moderate angular accuracy for the steering agents and limited communication between them, is also determined. Our proposed bearing-only algorithm offers crucial insights into swarm dynamics, which may have applications across a variety of domains, such as agriculture and search and rescue.This article is part of the theme issue 'The road forward with swarm systems'.

Chemical herding as a multiplicative factor for top-down manipulation of colloids

Colloidal particles can create reconfigurable nanomaterials, with applications such as color-changing, self-repairing, and self-regulating materials and reconfigurable drug delivery systems. However, top-down methods for manipulating colloids are limited in the scale they can control. We consider here a new method for using chemical reactions to multiply the effects of existing top-down colloidal manipulation methods to arrange large numbers of colloids with single-particle precision, which we refer to as chemical herding. Using simulation-based methods, we show that if a set of chemically active colloids (herders) can be steered using external forces (i.e., electrophoretic, dielectrophoretic, magnetic, or optical forces), then a larger set of colloids (followers) that move in response to the chemical gradients produced by the herders can be steered using the control algorithms given in this paper. We also derive bounds that predict the maximum number of particles that can be steered in this way, and we illustrate the effectiveness of this approach using Brownian dynamics simulations. Based on the theoretical results and simulations, we conclude that chemical herding is a viable method for multiplying the effects of existing colloidal manipulation methods to create useful structures and materials.

Herding stochastic autonomous agents via local control rules and online target selection strategies

We propose a simple yet effective set of local control rules to make a small group of “herder agents” collect and contain in a desired region a large ensemble of non-cooperative, non-flocking stochastic “target agents” in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. The effectiveness of the proposed approach is confirmed in both simulations in ROS and experiments on real robots.