Vision-based collective motion: A locust-inspired reductionist model

The behavioral mechanisms governing collective motion in swarming locusts

Collective motion, which is ubiquitous in nature, has traditionally been explained by "self-propelled particle" models from theoretical physics. Here we show, through field, lab, and virtual reality experimentation, that classical models of collective behavior cannot account for how collective motion emerges in marching desert locusts, whose swarms affect the livelihood of millions. In contrast to assumptions made by these models, locusts do not explicitly align with neighbors. While individuals respond to moving-dot stimuli through the optomotor response, this innate behavior does not mediate social response to neighbors. Instead, locust marching behavior, across scales, can be explained by a minimal cognitive framework, which incorporates individuals' neural representation of bearings to neighbors and internal consensus dynamics for making directional choices. Our findings challenge long-held beliefs about how order can emerge from disorder in animal collectives.

Visual collective behaviors on spherical robots

The implementation of collective motion, traditionally, disregard the limited sensing capabilities of an individual, to instead assuming an omniscient perception of the environment. This study implements a visual flocking model in a 'robot-in-the-loop' approach to reproduce these behaviors with a flock composed of 10 independent spherical robots. The model achieves robotic collective motion by only using panoramic visual information of each robot, such as retinal position, optical size and optic flow of the neighboring robots. We introduce a virtual anchor to confine the collective robotic movements so to avoid wall interactions. For the first time, a simple visual robot-in-the-loop approach succeed in reproducing several collective motion phases, in particular, swarming, and milling. Another milestone achieved with by this model is bridging the gap between simulation and physical experiments by demonstrating nearly identical behaviors in both environments with the same visual model. To conclude, we show that our minimal visual collective motion model is sufficient to recreate most collective behaviors on a robot-in-the-loop system that be implemented using several individuals, behaves as numerical simulations predict and is easily comparable to traditional models.

The visual stimuli attributes instrumental for collective-motion-related decision-making in locusts

Visual interactions play an instrumental role in collective-motion-related decision-making. However, our understanding of the various tentative mechanisms that can serve the visual-based decision-making is limited. We investigated the role that different attributes of the visual stimuli play in the collective-motion-related motor response of locust nymphs. We monitored and analyzed the behavioral responses of individual locusts tethered in a natural-like walking posture over an airflow-suspended trackball to carefully selected stimuli comprising various black rectangular shapes. The experimental findings together with a prediction model relating the level of behavioral response to the visual stimuli attributes indicate a major role of the number of objects in the visual field, and a further important effect of the object's vertical moving edges. While the object's horizontal edges can be utilized in the estimation of conspecifics' heading, the overall area or visual angle subtended by the stimuli do not seem to play any role in inducing the response. Our results offer important novel insights regarding the fundamental visual-based mechanisms underlying animal collective motion and can be useful also in swarm robotics.

Body orientation change of neighbors leads to scale-free correlation in collective motion

Collective motion, such as milling, flocking, and collective turning, is a common and captivating phenomenon in nature, which arises in a group of many self-propelled individuals using local interaction mechanisms. Recently, vision-based mechanisms, which establish the relationship between visual inputs and motion decisions, have been applied to model and better understand the emergence of collective motion. However, previous studies often characterize the visual input as a transient Boolean-like sensory stream, which makes it challenging to capture the salient movements of neighbors. This further hinders the onset of the collective response in vision-based mechanisms and increases demands on visual sensing devices in robotic swarms. An explicit and context-related visual cue serving as the sensory input for decision-making in vision-based mechanisms is still lacking. Here, we hypothesize that body orientation change (BOC) is a significant visual cue characterizing the motion salience of neighbors, facilitating the emergence of the collective response. To test our hypothesis, we reveal the significant role of BOC during collective U-turn behaviors in fish schools by reconstructing scenes from the view of individual fish. We find that an individual with the larger BOC often takes on the leading role during U-turns. To further explore this empirical finding, we build a pairwise interaction mechanism on the basis of the BOC. Then, we conduct experiments of collective spin and collective turn with a real-time physics simulator to investigate the dynamics of information transfer in BOC-based interaction and further validate its effectiveness on 50 real miniature swarm robots. The experimental results show that BOC-based interaction not only facilitates the directional information transfer within the group but also leads to scale-free correlation within the swarm. Our study highlights the practicability of interaction governed by the neighbor's body orientation change in swarm robotics and the effect of scale-free correlation in enhancing collective response.

Non-local interaction in discrete Ricci curvature-induced biological aggregation

We investigate the collective dynamics of multi-agent systems in two- and three-dimensional environments generated by minimizing discrete Ricci curvature with local and non-local interaction neighbourhoods. We find that even a single effective topological neighbour suffices for significant order in a system with non-local topological interactions. We also explore topological information flow patterns and clustering dynamics using Hodge spectral entropy and mean Forman-Ricci curvature.

Pausing to swarm: locust intermittent motion is instrumental for swarming-related visual processing

Intermittent motion is prevalent in animal locomotion. Of special interest is the case of collective motion, in which social and environmental information must be processed in order to establish coordinated movement. We explored this nexus in locust, focusing on how intermittent motion interacts with swarming-related visual-based decision-making. Using a novel approach, we compared individual locust behaviour in response to continuously moving stimuli, with their response in semi-closed-loop conditions, in which the stimuli moved either in phase with the locust walking, or out of phase, i.e. only during the locust's pauses. Our findings clearly indicate the greater tendency of a locust to respond and 'join the swarming motion' when the visual stimuli were presented during its pauses. Hence, the current study strongly confirms previous indications of the dominant role of pauses in the collective motion-related decision-making of locusts. The presented insights contribute to a deeper general understanding of how intermittent motion contributes to group cohesion and coordination in animal swarms.