Directed light beams are a promising means of control for microscopic agents, whether they are microrobots or phototatic microorganisms such as Volvox and ciliates. Given the simple reactive behaviors common to most microagents, there is likely to be a certain universality in light-beam algorithms that can usefully ‘herd’ such collectives around. Here, we develop three light-beam control algorithms to herd light-sensitive agents around a two-dimensional environment, each making varying assumptions about agent behavioral capacities. We test them with small swarms of Kilobot robots, which are about 3cm in size. These robots are convenient macro-scale demonstrators of possibilities at the micro-scale. The algorithms are tested in simulation and found to achieve the desired herding goals. Waypoint following missions were implemented using single robots and multiple robots to demonstrate more complex trajectories and highlight the effects of multiple robots interacting. One of the algorithms was tested with real robots and is shown to perform well, owing to good robustness to projection inaccuracies. Future swarm engineers could refer to a common toolbox of broadly effective light-based swarm control algorithms, which can be selected according to agent capabilities.

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such ‘swarm’ systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a ‘multi-armed bandit’ problem). House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralized estimation of the probability that each choice is best. Viewed this way, their behavioral algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our nest finding model incorporates insights from approximate Bayesian computation as a model of colony-level behavior; and particle filtering as a model of Temnothorax ‘tandem running’. Our framework suggests that the mechanisms of complex collective behavior can sometimes be explained as a spatial enactment of Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviors when collective decisions must be made. It also points to the potential for bioinspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered systems, such as robot swarms.

Robot swarms can solve tasks that are impossible or too hazardous for single robots. For example, following a nuclear radiation leak, a user may wish to establish a distributed communication chain that partly extends into the most dangerous areas to gather new information. The challenge is to create long chains while maintaining chain connectivity (‘connected reach’), where those at the distant end of the chain are more likely to be disconnected. Here we take the concept of dynamic ‘boldness’ levels from animal behavior ( Stegodyphus social spiders) to explore such risky environments in a way that adapts to the size of the group. Boldness is implemented as a continuous variable associated with the risk appetite of individuals to explore regions more distant from a central base. We present a decentralized mechanism for robots, based on the frequency of their social interactions, to adaptively take on ‘bold’ and ‘shy’ behaviors. Using this new bioinspired algorithm, which we call SPIDER, swarms are shown to adapt rapidly to the loss of bold individuals by regenerating a suitable shy–bold distribution, with fewer bolder individuals in smaller groups. This allows them to dynamically trade-off the benefits and costs of long chains (information retrieval versus loss of robots) and demonstrates the particular advantage of this approach in hazardous or adversarial environments.