Flying social insects can provide model systems for in-flight interactions in computationally-constrained aerial robot swarms. The social interactions in flying insects may be chemically modulated and quantified via recent measurement advancements able to simultaneously make precise measurements of insect wing and body motions. This paper presents the first in-flight quantitative measurements of ethanol-exposed honey bee body and wing kinematics in archival literature. Four high-speed cameras (9000 frames/sec) were used to record the wing and body motions of flying insects (Apis mellifera) and automated analysis was used to extract 9000 frame/sec measurements of honey bees’ wing and body motions through data association, hull reconstruction, and segmentation. The kinematic changes induced by exposure to incremental ethanol concentrations from 0% to 5% were studied using statistical analysis tools. Analysis considered trial-wise mean and maximum values and gross wingstroke parameters, and tested deviations for statistical significance using Welch’s t-test and Cohen’s d test. The results indicate a decrease in maximal heading and pitch rates of the body, and that roll rate is affected at high concentrations (5%). The wingstroke effects include a stroke frequency decrease and stroke amplitude increase for 2.5% or greater concentrations, gradual stroke inclination angle increase up to 2.5% concentration, and a more planar wingstroke with increasing concentration according to bulk wingstroke analysis. These ethanol-exposure effects provide a basis to separate ethanol exposure and neighbor effects in chemically mediated interaction studies.

Individual insects flying in crowded assemblies perform complex aerial maneuvers by sensing and feeding back neighbor measurements to small changes in their wing motions. To understand the individual feedback rules that permit these fast, adaptive behaviors in group flight, both experimental preparations inducing crowded flight and high-speed tracking systems capable of tracking both body motions and more subtle wing motion changes for multiple insects in simultaneous flight are needed. This measurement capability extends tracking beyond the previous focus on individual insects to multiple insects. This paper describes an experimental preparation that induces crowded insect flight in more naturalistic conditions (a laboratory-outdoor transition tunnel) and directly compares the resulting flight performance to traditional flight enclosures. Measurements are made possible via the introduction of a multi-agent high speed insect tracker called Hi-VISTA, which provides a capability to track wing and body motions of multiple insects using high speed cameras (9000–12 500 fps). Processing steps consist of automatic background identification, data association, hull reconstruction, segmentation, and feature measurement. To improve the biological relevance of laboratory experiments and develop a platform for interaction studies, this paper applies the Hi-VISTA measurement system to Apis mellifera foragers habituated to transit flights through the transparent transition environment. Binary statistical analysis (Welch’s t-test, Cohen’s d effect size) of 95 flight trajectories is presented, quantifying the differences between flights in an unobstructed environment and in a confined tunnel volume. The results indicate that body pitch angle, heading rate, flapping frequency, and vertical speed (heave) are each affected by confinement, and other flight variables show minor or statistically insignificant changes. These results form a baseline as swarm tracking and analysis begins to isolate the effects of neighbors from environmental enclosures, and improve the connection of high speed insect laboratory experiments to outdoor field experiments.

Flying social insects can provide model systems for inflight interactions in computationally-constrained aerial robot swarms, whose interactions may be chemically modulated under recent measurement advancements provide a capability to simultaneously make precise measurements of insect wing and body motions. This paper presents the first quantitative measurements of ethanol-exposed honey bee flight body and wing kinematic parameters. Four high speed cameras (9000 fps) were used to track the wing and body motions of insects ( Apis mellifera ). Digitization, consisting of data association, hull reconstruction, and segmentation, achieved the first high speed measurements of ethanol exposed honey bees’ wing and body motions. Kinematic changes induced by exposure to ethanol concentrations from 0% to 5% were studied using statistical analysis tools. Analysis considered trial wide mean and maximum values and gross wingstroke parameters, and tested deviations for statistical significance using Welch’s t-test and Cohen’s d test. The results indicate a decrease in maximal heading and pitch rates of the body, and that roll rate is affected at high concentrations (5%). The wingstroke effects include a stroke frequency decrease, stroke amplitude increase, stroke inclination angle increase, and a more planar wingstroke. These effects due to ethanol exposure are valuable tools to separate from interaction effects.

Individual insects flying in crowded assemblies perform complex aerial maneuvers by sensing and feeding back neighbor measurements to small changes in their wing motions. To understand the individual feedback rules that permit these fast, adaptive behaviors in group flight, a high-speed tracking system is needed capable of tracking both body motions and more subtle wing motion changes for multiple insects in simultaneous flight. This capability extends tracking beyond the previous focus on individual insects to multiple insects. This paper presents Hi-VISTA, which provides a capability to track wing and body motions of multiple insects using high speed cameras (9000 fps). Processing steps consist of automatic background identification, data association, hull reconstruction, segmentation, and feature measurement. To improve the biological relevance of laboratory experiments and develop a platform for interaction studies, this paper applies the Hi-VISTA measurement system to Apis mellifera foragers habituated to transit flights through a transparent tunnel. Binary statistical analysis (Welch’s t-test, Cohen’s d effect size) of 95 flight trajectories is presented, quantifying the differences between flights in an unobstructed tunnel and in a confined tunnel volume. The results indicate that body pitch angle, heading rate, flapping frequency, and vertical speed (heave) are all affected by confinement, and other flight variables show minor or statistically insignificant changes. These results form a baseline as swarm tracking and analysis begins to isolate the effects of neighbors from environment.

Collective motion in biological swarms emerges from local interactions among individuals with limited sensing and cognition, offering valuable insights for decentralized engineered systems. This study presents a systematic framework for extracting interaction graphs from multiagent trajectory data. Three graph identification methods, sparse regression, Granger causality, and cross-correlation, together with geometric anisotropy analysis, are applied to experimental recordings of honey bees during scattered and focused return-to-hive behaviors, aggregations of midges, and flocking passerine jackdaws. Connectivity analysis reveals task-dependent interaction structures: scattered honey bees exhibit causal interactions with up to ten neighbors, whereas focused bees interact with only two to three agents, with midges showing slightly lower connectivity. Cross-correlation analysis indicated honey bees exhibit distance-sensitivity in close proximity, while flocking birds exhibit a distance-dependent correlation decline. These results highlight diverse coordination mechanisms relevant to bio-inspired networked systems.

Understanding the interaction architectures that individual insects implement in group flight contributes to mathematics, biology, and robotics, including enabling dynamic aerial swarming. This study analyzes 1,000 trajectories of flying honeybees in crowded conditions approaching a stimulus and finds a dominant flight coordination architecture of ``dyadic" interactions and a new three-zone decision-making process. The experiment measures individual insect positions via an optical tracking system recording honeybees returning to a robotically-actuated hive entrance. Neighborhood analysis through three methods (cross-correlation, distance threshold, and average distance threshold) reveals the dominant interaction is dyadic, consisting of transient leader-follower behaviors embedded in the larger collective. The followers' update rules are then tested against three regulation candidates (control strategies by which the follower adjusts its motion: optic flow, relative velocity, and ``optical expansion rate") to minimize root mean square error. The results show that in each dyad, the follower proceeds through a three-stage process involving a change to feedback rules that is separated by an intermediate unregulated period. An insect initially maintains a consistent (less than 8\% variation) optical expansion rate until the inter-agent distance is as small as 10 cm. The regulation candidates then undergo large variations during an observation/decision zone lasting an average of 1.04 seconds. 79\% of followers entering the decision zone then re-engage to track the same initial leader while 21\% disengage. Upon re-engagement, the follower regulates inter-agent relative velocity, consistent with a closed-loop feedback proportional-integral (PI) controller regulating velocity tracking error. Proportional gain showed low variability across individuals, while derivative gain was found negligible and integral gain varied by individual. These findings highlight an alternative swarm architecture incorporating individual decision-making, feedback regulation target changes, and the presence of three interaction timescales.