Imraan A. Faruque

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.

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.

Collective motions in biological organisms such as honey bees, midges, and birds exhibit remarkable coordination and adaptability, emerging from local interactions among individuals, including those with tight constraints on neural material. Understanding the underlying mechanisms behind the collective behavior of biological swarms informs the dynamics of decentralized systems, including engineered aerial swarms. This study provides a systematic graph network extraction formulation applicable to physical domains trajectories including the practical effects of data dropouts, multiple agents, and multiple degrees of freedom. The analysis uses graph network modeling (in which nodes represent agents and edges denote interactions) to uncover the connectivity patterns governing swarm dynamics and quantify their differences. Three network identification methods, including sparse regression, Granger-causality, and crosscorrelation, are applied to determine the underlying graph from measured animal trajectories. An additional geometric anisotropy analysis used in bird flocks is also applied. The methods are applied to experimental recordings of honey bees involving scattered and focused “return-to-hive” behaviors, to midges in reproductive-related aggregations, and to flocking passerine birds. Across these studies, the number of agents a focal agent receives information from was quantified by analyzing the connectivity of identified interaction graphs. The results reveal honey bees show significant differences in connectivity and slight differences in anisotropy between experiments involving scattered and focused “return-to-hive” behaviors. In particular, scattered honey bees showed causal relationships up to 10 neighbors (consistent with flocking jackdaw data), while focused honey bees showed connections with up to 2-3 agents, and midges showed slightly lower connectivity. Finally, cross-correlation analysis indicated honey bees exhibit distance-sensitivity in close proximity, while flocking birds exhibit a distance-dependent correlation decline. Together, these results provide evidence for task-dependent in-degree modulation in honey bees, and provide a systematic analysis formulation that highlights the variety of approaches to coordinated motion, including those with and without geometric anisotropy. These results inform the design of bioinspired networked systems.

Systematic descriptions of the underlying interaction rules that insects use to support group and swarm flight has the potential to contribute to mathematics, biology, and robotics, including aerial swarming under sensory and computational limitations. This study analyzes 1,000 trajectories of flying honeybees in crowded conditions approaching a moving stimulus and finds how during this stimulus, honeybees coordinate flight through pairwise interactions involving a novel three-zone decision-making process. The experimental setup consists of 3-D position reconstructions via a high speed camera system recording honeybee foragers returning to a hive entrance actuated to move robotically. The analysis consists of neighborhood identification through three methods (cross-correlation, distance threshold, and average distance threshold), which reveals the dominant interaction is pairwise. The individual leader-follower pair interactions are then tested against three regulation candidates: optic flow, relative velocity, and optical expansion rate, based on minimizing root mean square error. The results show that each follower demonstrates a three stage process involving a feedback rule change, linked by an intermediate observation/decision phase. During the initial “lock” phase, an insect maintains a consistent optical expansion rate until inter-agent distance closes to 10 cm. The regulation candidates then undergo large variations during a relatively long observation/decision zone, with 1.04 seconds being the average time in the decision zone. 79% of the paired insect entries into the decision zone result in subsequent re-engagement to track the same initial leader, while 21% result in disengagement from the group behavior. Visual regulation candidate comparison in the third stage indicates that upon re-engagement, the follower relative velocity is regulated to provide consistent velocity matching between agents. The third stage’s velocity tracking is consistent with a closed-loop feedback proportional-integral (PI) controller regulating velocity tracking error. Across the insect population studied, the proportional gain remained showed minimal variability over individuals, a derivative gain was considered and found negligible, and the integral gain varied by individual. Collectively, these findings underscore the existence of an alternative swarm architecture, highlighting individual decision-making capabilities, feedback regulation target changes, and the presence of reactive, deliberative, and moderate (PI control) timescale interaction rules contained within aerial groups.

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), STMR achieves collective group motion. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.

This paper introduces the powerline unmanned surfer (PLUS) concept to extend the limited endurance of fixed wing unmanned aerial vehicles (UAVs) via in-flight energy harvesting from overhead electrical distribution power lines, and develops the flight dynamics and control framework to support centimeter-scale longitudinal powerline frequency tracking. The dynamics framework models the UAV's shape adaptive structure, aerodynamic forces, and control inputs, and applies the coupled flight mechanics framework to low clearance tracking of powerline contours. This study develops a "trajectory to shape-adaptive UAV" controller design approach for longitudinal powerline tracking through local spatial frequency matching. The frequency-matching approach dynamically regulates the aircraft modes' frequency to the powerline catenary spatial frequency using a generalized parameter linearization approach. Performance is assessed on an example UAV implementing camber and thickness morphing by quantifying clearance distance from neighborhood to high voltage powerline environments and across span and chord combinations. This approach achieves alternating periods of low-clearance tracking and antiphase oscillation, with 34% of the powerline having tracking error less than 1m. Airfoil thickness increase has a stronger effect over Phugoid mode wavelength than thickness decrease. Wingspan, chord, and powerline parameter sensitivities are quantified, providing a foundation for near-field long-distance powerline tracking and energy harvesting.

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), collective group motion can be achieved. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.

This study presents the feasibility of implementing commercial-off-the-shelf reciprocating piston compressors for a developed active flow control (AFC) actuation framework to quantify the aerospace relevant performance ramifications of control architecture and compressor operational choices. Three architectures are studied: supply valve metering, exit area metering, and a combined approach. All concepts are studied under varying compressor operation schedules. The analysis framework in this study integrates internal pneumatic actuation and discharge dynamics, an experimentally calibrated compressor pressure and thermal dynamics model, three feedback control architectures, and flight dynamics models. The framework is implemented in simulation to provide a user-friendly tool for linking AFC architecture choices to achievable flight trajectories. Actuator performance is evaluated using actuation time, output, compressor duty cycle, and specific energy consumption. Aircraft tracking performance is evaluated as usable time and slalom centerline deviation. The analysis indicates that a supply-volume-based metering approach is comparatively inefficient concerning an exit-area-based metering, resulting in high flight tracking error. Exit area metering provides the best efficiency and run time with some structural drawbacks, while the combined approach provides the best flight tracking performance at the expense of additional complexity. These results inform the choice of onboard AFC hardware choices.

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.