Bioinspiration & Biomimetics

Large commercial and military aircraft can operate in a wide range of turbulent conditions, except during extreme weather events such as cyclones. Smaller man-made vehicles, such as micro aerial vehicles (MAVs) and nano aerial vehicles (NAVs), are significantly more sensitive to routine environmental wind fluctuations, making them difficult to control. In contrast, insects exhibit remarkable stability in naturally gusty conditions. Despite this, few studies have systematically investigated the impact of gusts and turbulence on insect flight performance. To address this gap and to gain fundamental insights into insect flight stability under gusty conditions, we examined the flight of freely flying black soldier flies subjected to a discrete head-on aerodynamic gust in a controlled laboratory environment. Flight motions were recorded using two high-speed cameras, and body and wing kinematics were analyzed across 14 distinct cases. In response to the gust, we observed consistent features across the cases: (1) asymmetry in wing stroke amplitude, (2) large changes in body roll angle--up to 160{degrees}--occurring over approximately two wing beats ([~]20 ms) with recovery over [~]9 wing beats, (3) transient pitch-down attitude, and (4) deceleration in the flight direction. These rapid responses, combining passive and active control mechanisms, provide insight into the flight control strategies employed by insects. The findings offer valuable guidance for the design of MAVs and NAVs capable of robustly responding to gusts and unsteady airflow in natural environments.

Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fishs size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish-turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

Despite much of the literature perceiving fish schooling as an organized system with a focus on fixed formations for theoretical analyses, experimental observations suggest that frequent positional rearrangement commonly occurs. Previous studies have also demonstrated that fish schools reduce locomotor costs relative to individuals swimming alone. This introduces an intriguing dichotomy. How can individual fish within schools exhibit dynamic interactions while also saving energy? We hypothesize that schooling dynamics are the result of positional and kinematic modulation of individuals responding to fluid dynamic stimuli from the movement of neighbouring individuals. We propose a two-tier approach to studying kinematic modulation within fish schools. First, quantification of the variation of individual movement in a school relative to that of a solitary individual uses an analytical pipeline combining artificial-intelligence-enabled tracking and video processing. Second, the study of kinematic modulation in response to hydrodynamic stimuli uses a mechanical flapping mechanism coupled with an enclosure to control fish position. We discovered that fish in schools exhibit higher levels of positional and kinematic modulation than individuals swimming alone. Fish swimming in enclosures can robustly respond to fluid stimuli from either a simple robotic fish or other fish located in proximity. This two-tier approach allows high-resolution analysis of positional and kinematic modulation within fish schools and their impacts on energy conservation resulting from collective movement.

Body orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation directly from translational flight trajectory data. We trained a fully connected feed-forward artificial neural network (ANN) on a dataset in which both flight trajectory and body orientation were recorded simultaneously in freely flying Drosophila, using a time-delay embedding of ground velocity, air velocity, and inferred thrust vectors as input features. To improve generalization across arbitrary coordinate frames, we augmented the training data with random rotational transformations. Evaluated on a withheld test set of 3,313 trajectories (101,576 frames), the rotation-augmented model achieved a median mean absolute angular error of 10.51{degrees}, with accurate heading recovery across the full [-{pi}, {pi}) range. The estimator provides a practical tool for recovering body orientation information from existing trajectory datasets in which only center- of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data.

Birds are capable of performing elaborate flight maneuvers in variable environmental conditions. While flight is an adaptable and skilled motor behavior, we know surprisingly little about how birds master this ability. Across species, skilled motor behaviors show practice-related changes or improvements in performance and understanding which features of a motor behavior change with learning can lend insight into the constraints, flexibility, and optimization of motor behavior. Here, we combined high-speed video recordings and pose-tracking software to analyze the kinematics of thousands of flights in zebra finches over multiple days of flight training and over different distances. Small birds, such as the zebra finch, use an intermittent flap-bounding flight style that alternates between flapping phases and flexed-wing bounding phases. We found that birds increase their flight speed and the time spent bounding and reduce variability in the bound position over ten days of flight performance. These motor changes to flight show savings, as performance is maintained even after two months without flight experience. Moreover, these same parameters are adjusted when birds fly longer distances, indicating that they may be key to flight flexibility. We built kinematic models to determine what features birds might be optimizing toward with learning and found that the data was best fit by a model simultaneously optimizing for minimum energy and flight duration. Taken together, our data highlight that flap-bounding flight shows hallmarks of skilled motor learning and lend new insight into the function of bounds.

Walking on sloped terrain requires substantial mechanical and control adaptations for effective energy management compared to level ground locomotion. The Virtual Pivot Point (VPP) hypothesis explains sagittal plane angular momentum regulation during level walking, but its validity in slope walking remains unexplored. This study combines human experiments with template-model simulations to investigate how the VPP strategy is modulated during slope walking. Participants walked on an instrumented ramp at various inclinations (0{degrees}, {+/-} 7.5{degrees}, {+/-} 10{degrees}), while a 2D spring-loaded inverted-pendulum model with a trunk segment simulated the task. Experimental results confirmed that the VPP is a robust feature of slope walking (R2 > 0.975). The simulation reproduced the change in hip torque and trunk adaptations by modulating VPP position. Results of this study indicate that VPP position and trunk dynamics could afford stability and energy management on gentle slopes, but to robustly navigate steeper ramps, humans recruit a multi-joint strategy where the knee and ankle joints play a crucial role in managing the energetic demands of sloped terrain. Beyond advancing our understanding of locomotor control, these insights have practical implications for the design of exoskeletons that adapt to uneven terrain.

From facial expressions to gestures, animals use multiple signal modalities to express emotions and communicate. In dogs, tail movements are conspicuous behaviours associated with emotional states, but this link remains debated. We investigated canine emotional states underlying tail wagging by systematically analysing differences in tail movements in a computer-controlled task encompassing two non-social Conditions - Rewarded (positive) and Unrewarded (negative), and two Epochs (pre-response and outcome anticipation). Using pose-tracking we found that 11 out of 23 dogs did not wag their tails in at least 75% of trials, suggesting that some dogs may inherently wag less or that tail wagging is primarily a social signal. Our results showed that dogs were more likely to wag during positive anticipation; whereas in the negative condition, despite tail amplitude being more prominent, increased speeds reflected arousal rather than valence. Further work should assess tail kinematics in social contexts to test and extend these findings.

Nocturnal migration is a remarkable phenomenon observed in many insect species, including moths. Migratory moths are capable of maintaining precise directional orientation during migration, as demonstrated in both laboratory and field studies, suggesting that they use multiple environmental cues for orientation and navigation. Recent studies on Australian Bogong moths revealed that these animals can use stellar cues and likely the geomagnetic field (in conjunction with local visual cues) to select and maintain population-specific migratory direction. However, the underlying orientation mechanisms used by most other migratory moths are still largely unresolved. Further, it remains unclear whether migratory moths can adjust their orientation using Earths magnetic field parameters for determining their position relative to the goal (i.e. location or map information) - an ability clearly shown in some migratory birds which respond to virtual magnetic displacements by correcting their orientation (experiments when animals are exposed to magnetic cues corresponding to other geographic locations). Here, we present results from virtual magnetic displacement experiments conducted on red underwings (Catocala nupta). In addition, we tested their orientation under simulated overcast conditions and in a vertical magnetic field to get indications whether this species relies on geomagnetic or celestial cues to maintain its population-specific migratory direction. Our results show that (1) red underwings did not compensate for virtual magnetic displacement, indicating the absence of a magnetic map; (2) they remained significantly oriented in the absence of geomagnetic information, suggesting the use of a stellar compass; and (3) there was no evidence of magnetic compass orientation in absence of any visual cues.

Head rotation is the leading cause of diffuse brain injuries from cycling accidents, with severe, long-term or even fatal consequences. Here, we present a novel helmet safety technology, the Release Layer System (RLS), designed to enhance conventional helmets and reduce the likelihood of such injuries. RLS is located on the outer side of the helmet and thus gets impacted first. The force of the impact activates a rolling mechanism triggering the release of an outer polycarbonate panel, thereby dispersing and transforming a substantial portion of the incident rotational energy. To evaluate the effectiveness of the technology, we conducted oblique impact tests on three popular helmet types, in conventional and RLS-equipped configurations, at three impact locations. RLS-equipped helmets reduced Peak Angular Velocity (PAV) by 57-66%, averaged across impact locations, compared to their conventional counterparts. This corresponds to a 68-86% reduction in the probability of an AIS2+ brain injury, as estimated by the Brain Injury Criterion. The most notable improvement was observed at the pYrot location (front impacts, mid-sagittal plane), with up to 85% PAV reduction. Testing across headforms further demonstrated the effectiveness of the technology in mitigating head rotation irrespective of variations in evaluation setups. This work introduces a novel mechanism for rotational impact mitigation and provides evidence of its potential benefits compared with conventional helmets. As an outer-layer approach, RLS may offer an alternative pathway for managing rotational kinematics in future helmet designs.

Finding resources for the colony is one of the most difficult and risky tasks for a social insect worker. A worker on a foraging trip can face a number of challenges, including interference from other individuals, her own errors, and environmental disturbances. Collectively, colonies may use a variety of strategies to minimize the impact of such perturbations on the foraging process. Here, we investigated how individual Solenopsis xyloni ant workers react to perturbation of an established pheromone trail. We trained foragers from colonies in the field to either a low or high concentration sucrose solution in a feeder on a T-maze setup, then replaced a section of floor covering, removing a section of the pheromone trail previously laid. We found that while ants made correct choices on the T-maze when the trail was intact, their choices did not differ from chance when the trail was absent, indicating strong reliance on a pheromone trail (and not, for example, memory) to return to the resource. Moreover, when the trail was absent, we found that a majority of ants abandoned the resource, and that even the ants that were able to reach the resource did not repair the perturbed trail. However, with a high-quality resource, more ants persisted in attempting to reach it (instead of abandoning). We interpret these responses in the framework of robustness mechanisms discussed in systems biology. Our study thus links individual and collective responses to perturbations, and provides an empirical example of how information use interacts with system robustness. Statements and declarationsThe authors have no competing interests to declare that are relevant to the content of this article.

Organisms must manage a trade-off between robustness and flexibility as they enact adaptive behaviors. One way organisms achieve this is by navigating a network of quasi-stable behavioral states. Evidence for such behavioral states has been observed in many organisms, and new methods for detecting these states have taken on a prominent research focus. Although dynamical models demonstrating behavioral switching have been developed significantly over the past few decades, theories of the similarities and differences among these models, necessary for advancing empirical modeling, have not yet been fully elaborated. Here, we consider behavioral switching in two different classes of dynamical models of the forward-reversal behavioral transition in C. elegans. We first show how fundamentally different models can give rise to similar phenomena under noisy conditions. We then analyze the deterministic aspects of these models to expand on their differences, clarifying the theoretical relationship between them. Finally, we demonstrate how sequence models can be further extended to incorporate dwell times for behavioral states. Our work contributes toward a broader theoretical understanding of behavioral switching in adaptive systems.

Optimal foraging behavior is a key component of successful adaptations to natural environments. Understanding how animals decide to stay near food or to leave it for another food patch gives us insights into the underlying cognitive mechanisms that govern adaptive behaviors. 3D pose tracking was used to determine how pigeons exploit a 4 square meter arena with two separate platforms (i.e. food patches) whose absolute and relative elevations were manipulated. Detailed kinematic features of foraging and traveling behaviors were quantified using automated video tracking, without a need for manual coding. Our computational approach captured continuous, high-dimensional movement patterns and enabled precise quantification of travel costs between patches. Combined with mixed-effects survival analysis, our fine-grained behavioral tracking provided detailed insight into the moment-by-moment dynamics of patch-leaving decisions of pigeons. As expected from behavior optimization models, our results showed a preference to visit a ground food platform first, and longer latencies to leave an elevated platform. Foraging activity significantly decreased throughout the session, with shorter visits, less pecks per visit, and a decrease in inter-peck variability. However, a mixed-effects Cox regression modeled pigeons patch-leaving probability, demonstrating that current and cumulative foraging parameters between patches significantly enhanced the models predictive power beyond patch accessibility (i.e., beyond travel costs). This suggests that pigeons integrate both current environmental cues and their individual foraging history when making patch-leaving decisions. Our findings are discussed in relation to the marginal value theorem and optimal foraging theory.

A passive device that attaches to the feet, called an exotendon, can reduce the energetic cost of running at moderate speeds, but its efficacy and optimal design parameters at higher speeds are unknown. Identifying optimal parameters at new speeds experimentally would require many experimental trials with different exotendon designs, which is challenging for participants at higher running speeds. We developed a muscle-driven simulation framework to predict the effect of various exotendon designs on the energetic cost of running at an experimentally untested speed (4 m/s). We used these predictions to select four designs, which we evaluated experimentally as users ran at this speed. The framework correctly predicted that an exotendon that reduced energetic cost at 2.7 m/s would also reduce energetic cost at 4 m/s (10% predicted vs. 5.7% measured) and that a short, stiff exotendon and a long, compliant exotendon would not significantly reduce energetic cost. However, exotendon parameters predicted by the simulation to maximize energetic savings did not significantly reduce energetic cost when evaluated experimentally. There was variability between participants in both the magnitude of maximum energy savings and the exotendon condition associated with those savings. In a 5-km time trial performed with and without the exotendon condition that elicited the largest energy savings for each participant during the experiment, we observed a lower average heart rate (-3.9 {+/-} 3.8 beats/min; P=0.03; mean {+/-} standard deviation) and increased cadence (15.9 {+/-} 9.6 steps/min; P=0.002) when participants ran with the exotendon but did not observe a statistically significant difference in finishing time (-13.5 {+/-} 24.6 sec; P=0.3). These results demonstrate exotendons can reduce energetic cost across multiple running speeds and that predictive simulations provide a framework for guiding experiments to evaluate assistive device designs. Author summaryDesigning assistive devices that help people move more efficiently usually requires many experimental trials. These studies can be time-consuming and physically demanding, especially when testing multiple device designs. In this study, we explored whether computer simulations could help guide the design of an assistive device for running called an exotendon. The exotendon is a simple elastic band that connects the feet and can help runners use less energy. Previous experiments showed that the device reduces the energy needed to run at moderate speeds, but it was unclear whether it would also work at faster speeds or which design would lead to energetic savings. We first used simulations of human running to test many possible exotendon designs at a faster speed. These simulations allowed us to identify promising designs before conducting experiments. We then tested a small number of these designs with runners. The experiments confirmed that the exotendon can reduce the energy required to run at faster speeds, although the efficacy of different designs varied between individuals. Our results show that computer simulations can help researchers rapidly evaluate a variety of assistive device ideas and focus experimental testing on the most promising designs.

Understanding how environmental stressors affect pollinator behavior is essential for assessing ecosystem health. Automated flower and robotic feeder systems (AFRFSs) have transformed how pollinator foraging and learning are studied. Still, most systems remain reward-centric, limiting their ability to probe aversive learning and nociception under field conditions. Here, we present FloBuzz, a modular AFRFS that couples automated reward delivery with computer-vision-based visit detection to trigger closed-loop electric-shock stimulation in free-flying honey bees. In our setup, FloBuzz consisted of a 3D-printed feeder with a shock grid, a syringe pump with fluid-level feedback, and an electric shock stimulus trigger module that applied user-defined shock patterns. In a proof-of-concept trial that alternated between shock-free, 6 V, and shock-free, 9 V intervals, bee visitation increased over time during shock-free periods but declined during shock periods, with a steeper decline at 9 V than at 6 V, demonstrating a voltage-dependent avoidance. By enabling programmable, time-resolved aversive stimulation at an artificial flower in outdoor conditions, FloBuzz expanded AFRFS capabilities beyond purely reward-based paradigms. Evidently, FloBuzzs modular design will permit the investigation of diverse behavioral paradigms, including studies of toxin exposure, cognitive plasticity, and reward processing.

Understanding how fish navigate complex natural environments requires bridging fine-scale biomechanics with ecological behavior. We investigated the volitional movement and energetics of wild red drum (Sciaenops ocellatus) across laboratory, mesocosm, and field settings. Using flow-respirometry, we quantified metabolic costs and swimming kinematics under ecologically relevant flow conditions shaped by bluff bodies mimicking mangrove roots and oyster mounds. Fish swimming in turbulent wakes exhibited reduced oxygen consumption and altered tailbeat dynamics, especially at high flow speeds. In a large outdoor mesocosm, dual accelerometers revealed a rich behavioral repertoire, including maneuvering and rest, which is not easily observable in confined lab settings. Spectral analysis and clustering identified eight distinct locomotory states, highlighting the limitations of summed acceleration metrics. Field telemetry tracked wild red drum across a 54 km estuarine corridor for a three-year period through an array of 36 acoustic receivers, revealing movement patterns shaped by tidal flow and physical habitats. Hydrodynamic modeling revealed that while laboratory trials demonstrated substantial energetic savings at high flows (approaching 100 cm/s), wild fish were detected predominantly in low-velocity microhabitats (<30 cm/s) near structurally complex features. This mismatch suggests that habitat selection is an adaptive strategy driven by ecological factors such as foraging opportunities, predation refuge, and site fidelity, rather than hydrodynamic efficiency alone. Our multi-scalar approach demonstrates that while flow-structure interactions can reduce locomotor costs for fish, habitat use in the wild reflects broader ecological constraints, offering a framework for integrating biomechanics, physiology, and ecology in conservation-relevant contexts.

Wearable devices for gravity balancing have high potential for impact across domains, including neuromotor rehabilitation and occupational systems. Devices made from compliant mechanisms, optimized to achieve specific compensation moments at target joints, have proven effective, but thus far have solely been optimized towards gravity compensation and not other wearability criteria. In this work, we propose a multi-objective optimization framework, based on particle swarm optimization, to design a soft, gravity balancing shoulder orthosis, while taking into account wearability constraints such as undesired loading directions and device size. Using this custom framework, we pursued multiple stages of orthosis design and optimization, selecting multiple solutions to be translated to real-world prototypes. These solutions were realized via 3D printing with thermoplastic polyurethane and evaluated for mechanical performance on benchtop and in-vivo. In-vivo testing on 6 healthy individuals demonstrated relative reductions in muscle activity for the anterior deltoid and upper trapezius, by 53 % and 71 % respectively when operating the orthosis for static tasks within functional shoulder ranges of motion. Changes in muscle activation were also were observed across other muscles, including the posterior deltoid, as well as in dynamic tasks at different speeds.

Collective decision making is a fundamental aspect of group behavior in both animals and humans, and often involves reaching a consensus on the best of n options, using empirical evidence. Although many parallels have been drawn between human and animal collective decisions, collective human behavior is rarely studied in the type of embodied scenarios that animals are often faced with. In this study, we placed human groups in a virtual setup similar to nest site selection in social animals, in which they explored a shared environment and reached a consensus based on their observations of empirical features. In groups of up to 10, participants had to reach consensus on the empirically largest of four candidate sites without verbal communication, instead using movement-based interactions in a custom-developed 3D virtual environment for online multi-participant experiments. The results showed that the speed and accuracy of consensus was importantly modulated by perceptual difficulty and information availability, but that no speed-accuracy trade-off was present. Participants attempted to reach consensus on the empirically largest site by flexibly adapting their use of social information to perceptual difficulty, their spatial position, and the time already spent supporting some option. When a minority of informed individuals were present, these individuals exercised greater independence and influenced the group to faster and more accurate consensus. These results extend previous findings on social decision making strategies in humans to nonverbal scenarios akin to those of social insects.

Optimizing assistive wearable devices is crucial for their efficacy and user adoption, yet state-of-the-art methods like Human-in-the-Loop Optimization (HILO) and biomechanical modeling face limitations. HILO is time-consuming and often restricted to optimizing control parameters, while inverse dynamics assumes invariant kinematics, which is unreliable for adaptive human-device interaction. Predictive simulation offers a powerful alternative, enabling computational exploration of design spaces. However, existing approaches often lack systematic optimization frameworks and rigorous validation against experimental data. To address this, we developed a Design Optimization Platform that integrates predictive simulations within a two-level optimization structure for personalizing assistive device design. This paper primarily validates the platforms predictive simulations against a publicly available dataset of the passive Biarticular Thigh Exosuit (BATEX), assessing its reliability. Our findings show that the model can sufficiently predict the kinematics and major muscle activations, except for the pelvis tilt and some biarticular muscles. The key finding is that successful identification of personalized optimal BATEX stiffness parameters needs acceptable prediction of metabolic cost trends, not their precise values. Our analysis further reveals that the models accuracy in predicting Vasti muscle activation in the baseline condition is a significant indicator of its success in predicting metabolic cost trends. This demonstrates that accurate prediction of performance trends is more important for effective simulation-based design optimization than perfect biomechanical accuracy, advancing targeted and efficient assistive device development.

Neural feedback is important for healthy control of movement, and multiple neurological disorders (e.g., stroke, cerebral palsy, Parkinsons disease, incomplete spinal cord injury) can be described by how they impair healthy feedback or induce unhealthy feedback. Researchers have created numerous computational neuromusculoskeletal models controlled by simulated neural feedback mechanisms, but these models rarely represent actual human subjects and thus have not found practical application in treating patients with movement impairments. As a step toward designing patient-specific treatments for individuals with neurological disorders, this study used the Neuromusculoskeletal Modeling Pipeline to develop and evaluate a novel synergy-based feedforward (FF)+feedback (FB) model using a personalized, three-dimensional neuromusculoskeletal walking model of an actual human subject post-stroke. Experimental walking data collected from the subject were used to create the subjects personalized walking model. This model was used to calculate lower body muscle activations consistent with the subjects electromyographic, joint motion, and ground reaction data for 5 calibration walking cycles. Nominal FF synergy controls were calculated by averaging the muscle synergies that closely reconstructed the 5 cycles of muscle activations and associated joint moments simultaneously. These nominal FF controls were then scaled by 0, 25, 50, 75, 100, and 125%, and the gap in reproducing individual cycle muscle activations was filled by fitting FB synergy controls as a function of joint positions, velocities, and moments as surrogates for muscle lengths, muscle velocities, and tendon forces. Finally, the six synergy-based FF+FB models controlled the subjects personalized walking model in predictive simulations performed for 3 testing walking cycles withheld from calibration. The 100% FF model (which still had minimal FB) reproduced the testing walking cycles the most closely, and only the 75%, 100%, and 125% FF models generated near-periodic walking motions using initial conditions consistent with experimental values. The 0, 25, and 50% FF models could generate near-periodic walking motions only when the initial conditions were allowed to diverge substantially from experimental values. Our findings suggest that predictive simulations of walking using real experimental data may require a minimum level of feedforward control and sufficient fitting data to predict a subjects actual dynamically consistent motion.

O_LIAccurate spatial localisation of free-flying echolocating bats is foundational for resolving fine-scale flight behaviour, prey interception, and spatial decision-making in natural environments. Acoustic localisation using microphone arrays is widely employed for this purpose, yet array geometries in field studies are typically chosen heuristically rather than systematically optimised. As portable multichannel ultrasonic recording systems become increasingly accessible, principled design guidelines are needed to ensure reliable localisation performance under practical deployment requirements. C_LIO_LII introduce an iterative array optimisation algorithm that designs microphone geometries by maximising localisation reliability within a predefined three-dimensional field of interest. The method evaluates candidate geometries using simulated acoustic emissions and time-difference-of-arrival localisation, quantifying performance as a volumetric pass rate: the proportion of source locations that meet a user-defined accuracy threshold. Microphone positions are iteratively perturbed and accepted based on improvements to this task-level metric, while enforcing practical constraints on array aperture, inter-sensor spacing, and deployability. C_LIO_LIAcross canonical polyhedral geometries, random initialisations, and arrays comprising four to twelve microphones, optimisation consistently produced rapid early gains followed by convergence to geometry-specific performance limits. Under fixed-aperture constraints, increasing the microphone count yielded diminishing returns, and optimised low-order arrays -- particularly four-microphone configurations -- matched or exceeded the volumetric localisation performance of higher-order arrays with suboptimal geometry. Analysis of optimisation trajectories further revealed that convergence dynamics scale with array order, whereas achievable volumetric performance is dominated by geometry rather than sensor number. C_LIO_LIThese results demonstrate that array geometry is the primary determinant of volumetric localisation reliability, and that efficient, portable arrays can be systematically designed using optimisation rather than heuristic rules. The proposed framework is broadly applicable to bioacoustic localisation problems beyond echolocating bats, including avian tracking, passive acoustic monitoring, and conservation-oriented sensing, and provides a general approach for designing task-optimised acoustic sensor arrays for a wide range of applications. C_LI