Three-Dimensional Time-Resolved Trajectories from Laboratory Insect Swarms

Aggregations of animals display complex and dynamic behaviour, both at the individual level and on the level of the group as a whole. Often, this behaviour is collective, so that the group exhibits properties that are distinct from those of the individuals. In insect swarms, the motion of individuals is typically convoluted, and swarms display neither net polarization nor correlation. The swarms themselves, however, remain nearly stationary and maintain their cohesion even in noisy natural environments. This behaviour stands in contrast with other forms of collective animal behaviour, such as flocking, schooling, or herding, where the motion of individuals is more coordinated, and thus swarms provide a powerful way to study the underpinnings of collective behaviour as distinct from global order. Here, we provide a data set of three-dimensional, time-resolved trajectories, including positions, velocities, and accelerations, of individual insects in laboratory insect swarms. The data can be used to study the collective as a whole as well as the dynamics and behaviour of individuals within the swarm

Searching for Effective Forces in Laboratory Insect Swarms

Collective animal behaviour is often modeled by systems of agents that interact via effective social forces, including short-range repulsion and long-range attraction. We search for evidence of such effective forces by studying laboratory swarms of the flying midge Chironomus riparius. Using multi-camera stereoimaging and particle-tracking techniques, we record three-dimensional trajectories for all the individuals in the swarm. Acceleration measurements show a clear short-range repulsion, which we confirm by considering the spatial statistics of the midges, but no conclusive long-range interactions. Measurements of the mean free path of the insects also suggest that individuals are on average very weakly coupled, but that they are also tightly bound to the swarm itself. Our results therefore suggest that some attractive interaction maintains cohesion of the swarms, but that this interaction is not as simple as an attraction to nearest neighbours

Comparing trotting and turning strategies on the quadrupedal Oncilla Robot

In this paper, we compare three different trotting techniques and five different turning strategies on a small, compliant, biologically inspired quadrupedal robot, the Oncilla. The locomotion techniques were optimized on the actual hardware using a treadmill setup, without relying on models. We found that using half ellipses as foot trajectories resulted in the fastest gaits, as well as the highest robustness against parameter changes. Furthermore, we analyzed the importance of using the scapulae for turning, from which we observed that although not necessary, they are needed for turning with a higher speed

Long-Range Acoustic Interactions in Insect Swarms: An Adaptive Gravity Model

The collective motion of groups of animals emerges from the net effect of the interactions between individual members of the group. In many cases, such as birds, fish, or ungulates, these interactions are mediated by sensory stimuli that predominantly arise from nearby neighbors. But not all stimuli in animal groups are short range. Here, we consider mating swarms of midges, which are thought to interact primarily via long-range acoustic stimuli. We exploit the similarity in form between the decay of acoustic and gravitational sources to build a model for swarm behavior. By accounting for the adaptive nature of the midges\u27 acoustic sensing, we show that our \u27adaptive gravity\u27 model makes mean-field predictions that agree well with experimental observations of laboratory swarms. Our results highlight the role of sensory mechanisms and interaction range in collective animal behavior. Additionally, the adaptive interactions that we present here open a new class of equations of motion, which may appear in other biological contexts

Collective Gradient Sensing in Fish Schools

Throughout the animal kingdom, animals frequently benefit from living in groups. Models of collective behaviour show that simple local interactions are sufficient to generate group morphologies found in nature (swarms, flocks and mills). However, individuals also interact with the complex noisy environment in which they live. In this work, we experimentally investigate the group performance in navigating a noisy light gradient of two unrelated freshwater species: golden shiners (Notemigonuscrysoleucas) and rummy nose tetra (Hemigrammus bleheri). We find that tetras outperform shiners due to their innate individual ability to sense the environmental gradient. Using numerical simulations, we examine how group performance depends on the relative weight of social and environmental information. Our results highlight the importance of balancing of social and environmental information to promote optimal group morphologies and performance

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO