Sensorimotor Model of Obstacle Avoidance in Echolocating Bats

Theoretical investigation of active listening behavior based on the echolocation of CF-FM bats

Bats perceive the three-dimensional environment by emitting ultrasound pulses from their nose or mouth and receiving echoes through both ears. To determine the position of a target object, it is necessary to know the distance and direction of the target. Certain bat species that use a combined signal of long constant frequency and short frequency modulated ultrasounds synchronize their pinnae movement with pulse emission, and this behavior has been regarded as helpful for localizing the elevation angle of a reflective sound source. However, the significance of bats' ear motions remains unclear. In this study, we construct a model of an active listening system including the motion of the ears, and conduct mathematical investigations to clarify the importance of ear motion in direction detection of the reflective sound source. In the simulations, direction detection under rigid ear movements with interaural level differences was mathematically investigated by assuming that bats accomplish direction detection using the amplitude modulation in the echoes caused by ear movements. In particular, the ear motion conditions required for direction detection are theoretically investigated through exhaustive simulations of the pseudo-motion of the ears, rather than simulations of the actual ear motions of bats. The theory suggests that only certain ear motions, namely three-axis rotation, allow for accurate and robust direction detection. Our theoretical analysis also strongly supports the behavior whereby bats move their pinnae in the antiphase mode. In addition, we suggest that simple shaped hearing directionality and well-selected uncomplicated ear motions are sufficient to achieve precise and robust direction detection. Our findings and mathematical approach have the potential to be used in the design of active sensing systems in various engineering fields.

Analysis of echolocation behavior of bats in "echo space" using acoustic simulation

BACKGROUND

Echolocating bats use echo information to perceive space, control their behavior, and adjust flight navigation strategies in various environments. However, the echolocation behavior of bats, including echo information, has not been thoroughly investigated as it is technically difficult to measure all the echoes that reach the bats during flight, even with the conventional telemetry microphones currently in use. Therefore, we attempted to reproduce the echoes received at the location of bats during flight by combining acoustic simulation and behavioral experiments with acoustic measurements. By using acoustic simulation, echoes can be reproduced as temporal waveforms (including diffracted waves and multiple reflections), and detailed echo analysis is possible even in complex obstacle environments.

RESULTS

We visualized the spatiotemporal changes in the echo incidence points detected by bats during flight, which enabled us to investigate the "echo space" revealed through echolocation for the first time. We then hypothesized that by observing the differences in the "echo space" before and after spatial learning, the bats' attentional position would change. To test this hypothesis, we examined how the distribution of visualized echoes concentrated at the obstacle edges after the bats became more familiar with their environment. The echo incidence points appeared near the edge even when the pulse direction was not toward the edge. Furthermore, it was found that the echo direction correlated with the turn rate of the bat's flight path, revealing for the first time the relationship between the echo direction and the bat's flight path.

CONCLUSIONS

We were able to clarify for the first time how echoes space affects echolocation behavior in bats by combining acoustic simulations and behavioral experiments.

A bio-mimetic miniature drone for real-time audio based short-range tracking

One of the most difficult sensorimotor behaviors exhibited by flying animals is the ability to track another flying animal based on its sound emissions. From insects to mammals, animals display this ability in order to localize and track conspecifics, mate or prey. The pursuing individual must overcome multiple non-trivial challenges including the detection of the sounds emitted by the target, matching the input received by its (mostly) two sensors, localizing the direction of the sound target in real time and then pursuing it. All this has to be done rapidly as the target is constantly moving. In this project, we set to mimic this ability using a physical bio-mimetic autonomous drone. We equipped a miniature commercial drone with our in-house 2D sound localization electronic circuit which uses two microphones (mimicking biological ears) to localize sound signals in real-time and steer the drone in the horizontal plane accordingly. We focus on bat signals because bats are known to eavesdrop on conspecifics and follow them, but our approach could be generalized to other biological signals and other man-made signals. Using two different experiments, we show that our fully autonomous aviator can track the position of a moving sound emitting target and pursue it in real-time. Building an actual robotic-agent, forced us to deal with real-life difficulties which also challenge animals. We thus discuss the similarities and differences between our and the biological approach.

Animals solve problems that are considered very difficult for human engineers. In this study, we aimed to mimic animals’ ability to localize and track a moving sound source in real time. We do so using a bio-inspired approach by developing a miniature electronic circuit with two ear-like microphones and a micro-processor that is placed on a miniature drone. The circuit detects ultrasonic signals that are typical for echolocating bats and it uses its two ‘ears’ to estimate the azimuth of the sound source and to steer the drone accordingly. The system is completely autonomous without external human intervention. We focus on bat signals as a proof of concept, but we can alter the electronics to suit other biological signals. Future research will include groups of multiple drones moving together based on acoustic signals as bats and some birds can do in nature.

Efficient encoding of spectrotemporal information for bat echolocation

In most animals, natural stimuli are characterized by a high degree of redundancy, limiting the ensemble of ecologically valid stimuli to a significantly reduced subspace of the representation space. Neural encodings can exploit this redundancy and increase sensing efficiency by generating low-dimensional representations that retain all information essential to support behavior. In this study, we investigate whether such an efficient encoding can be found to support a broad range of echolocation tasks in bats. Starting from an ensemble of echo signals collected with a biomimetic sonar system in natural indoor and outdoor environments, we use independent component analysis to derive a low-dimensional encoding of the output of a cochlear model. We show that this compressive encoding retains all essential information. To this end, we simulate a range of psycho-acoustic experiments with bats. In these simulations, we train a set of neural networks to use the encoded echoes as input while performing the experiments. The results show that the neural networks’ performance is at least as good as that of the bats. We conclude that our results indicate that efficient encoding of echo information is feasible and, given its many advantages, very likely to be employed by bats. Previous studies have demonstrated that low-dimensional encodings allow for task resolution at a relatively high level. In contrast to previous work in this area, we show that high performance can also be achieved when low-dimensional filters are derived from a data set of realistic echo signals, not tailored to specific experimental conditions.

We show that complex (and simple) echoes from real environments can be efficiently and effectively represented using a small set of filters. Critically, we show that high performance across a range of tasks can be achieved when low-dimensional filters are derived from a data set of realistic echo signals, not tailored to specific experimental conditions. The redundancy in echoic information opens up the opportunity for efficient encoding, reducing the computational load of echo processing as well as the memory load for storing the information. Therefore, we predict the auditory system of bats to capitalize on this opportunity for efficient coding by implementing filters with spectrotemporal properties akin to those hypothesized here. Indeed, the filters we obtain here are similar to those found in other animals and other sensing capabilities. Our results indicate that bats could exploit the redundancy in sonar signals to implement an efficient neural encoding of the relevant information.

Braitenberg Vehicles as Computational Tools for Research in Neuroscience

Valentino Braitenberg reported his seminal thought experiment in 1984 using reactive automatons or vehicles with relatively simple sensorimotor connections as models for seemingly complex cognitive processes in biological brains. Braitenberg's work, meant as a metaphor for biological life encompassed a deep knowledge of and served as an analogy for the multitude of neural processes and pathways that underlie animal behavior, suggesting that seemingly complex behavior may arise from relatively simple designs. Braitenberg vehicles have been adopted in robotics and artificial life research for sensor-driven navigation behaviors in robots, such as localizing sound and chemical sources, orienting toward or away from current flow under water etc. The neuroscience community has benefitted from applying Braitenberg's bottom-up approach toward understanding analogous neural mechanisms underpinning his models of animal behavior. We present a summary of the latest studies of Braitenberg vehicles for bio-inspired navigation and relate the results to experimental findings on the neural basis of navigation behavior in animals. Based on these studies, we motivate the important role of Braitenberg vehicles as computational tools to inform research in behavioral neuroscience.

Learning from animals: How to Navigate Complex Terrains

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths' policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth's policy in each different terrain, the moth's policy exhibits a high level of robustness across terrains.

Clutter negotiating ability in an ensemble of forest interior bats is driven by body mass

Differences in wing morphology are predicted to reflect differences in bat foraging strategies. Experimental tests of this prediction typically assess the relationship between wing morphology and a measures of flight performance on an obstacle course. However, studies have lacked measures of obstacle avoidance ability true scores, which may confound interpretation of ability across the range of presented tasks. Here, we used Rasch analysis of performance in a collision-avoidance experiment to estimate the ability of bat species to fly through vegetative clutter. We refer to this latent trait as 'clutter negotiating ability' and determined the relationships between clutter negotiating ability and wing morphology in 15 forest insectivorous bat species that forage in the densely cluttered rainforests of Malaysia. The clutter negotiating ability scores were quantified based on individual responses of each species to 11 different obstacle arrangements (four banks of vertical strings 10-60 cm apart). The tasks employed for the collision-avoidance experiment were reliable and valid, although Rasch analysis suggested that the experiment was too easy to discriminate completely among the 15 species. We found significant negative correlations between clutter negotiating ability and body mass, wingspan, wing loading and wing area but a positive significant correlation with wingtip area ratio. However, in stepwise multiple regression analyses, only body mass and wing loading were significant predictors of clutter negotiating ability. Species fell into clusters of different clutter negotiating ability, suggesting a potential mechanism for resource partitioning within the forest interior insectivorous ensemble.

Avoidance of non-localizable obstacles in echolocating bats: A robotic model

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence.

PRINCIPLES AND PATTERNS OF BAT MOVEMENTS: FROM AERODYNAMICS TO ECOLOGY

Movement ecology as an integrative discipline has advanced associated fields because it presents not only a conceptual framework for understanding movement principles but also helps formulate predictions about the consequences of movements for animals and their environments. Here, we synthesize recent studies on principles and patterns of bat movements in context of the movement ecology paradigm. The motion capacity of bats is defined by their highly articulated, flexible wings. Power production during flight follows a U-shaped curve in relation to speed in bats yet, in contrast to birds, bats use mostly exogenous nutrients for sustained flight. The navigation capacity of most bats is dominated by the echolocation system, yet other sensory modalities, including an iron-based magnetic sense, may contribute to navigation depending on a bat's familiarity with the terrain. Patterns derived from these capacities relate to antagonistic and mutualistic interactions with food items. The navigation capacity of bats may influence their sociality, in particular, the extent of group foraging based on eavesdropping on conspecifics' echolocation calls. We infer that understanding the movement ecology of bats within the framework of the movement ecology paradigm provides new insights into ecological processes mediated by bats, from ecosystem services to diseases.

A fully autonomous terrestrial bat-like acoustic robot

Echolocating bats rely on active sound emission (echolocation) for mapping novel environments and navigating through them. Many theoretical frameworks have been suggested to explain how they do so, but few attempts have been made to build an actual robot that mimics their abilities. Here, we present the ‘Robat’—a fully autonomous bat-like terrestrial robot that relies on echolocation to move through a novel environment while mapping it solely based on sound. Using the echoes reflected from the environment, the Robat delineates the borders of objects it encounters, and classifies them using an artificial neural-network, thus creating a rich map of its environment. Unlike most previous attempts to apply sonar in robotics, we focus on a biological bat-like approach, which relies on a single emitter and two ears, and we apply a biological plausible signal processing approach to extract information about objects’ position and identity.

Many animals are able of mapping a new environment even while moving through it for the first time. Bats can do this by emitting sound and extracting information from the echoes reflected from objects in their surroundings. In this study, we mimicked this ability by developing a robot that emits sound like a bat and analyzes the returning echoes to generate a map of space. Our Robat had an ultrasonic speaker mimicking the bat’s mouth and two ultrasonic microphones mimicking its ears. It moved autonomously through novel out-doors environments and mapped them using sound only. It was able to negotiate obstacles and move around them, to avoid dead-ends and even to recognize if the object in front of it is a plant or not. We show the great potential of using sound for future robotic applications.

Echo-acoustic scanning with noseleaf and ears in phyllostomid bats

The mammalian visual system is highly directional and mammals typically employ rapid eye movements to scan their environment. Both sound emission and hearing in echolocating bats are directional but not much is known about how bats use ear movements and possibly movements of the sound-emitting structures to scan space. Here, we investigated in a tightly controlled behavioural experiment how Phyllostomusdiscolor bats employ their echolocation system while being moved through differently structured environments: we monitored and reconstructed both a close-up of the facial structures in 3D, including the motile noseleaf and outer ears, and the sonar-beam of the bat while it was moved along reflectors. Despite the simple linear movement of the bats in the setup, the bats pointed their beam quite variably in azimuth with a standard deviation of about ±20 deg. This variation arises from yaw-type head rotations. Video analyses show that the bat's noseleaf twitches with every echolocation call. Second, we show that the bat's ears are raised to a rather stereotypical head-centred position with every echolocation call. Surprisingly, P. discolor can adjust the timing and the magnitude of these ear movements to the distance of the reflectors with millisecond precision. Our findings reveal echolocation-specific specialisations as well as general principles of scanning and stabilisation of a directional remote sense. The call-correlated movements of the facial structures may lead to a higher directionality of the echolocation system and may enable the bats to adjust their echo-acoustic gaze to dynamic environments.

Place recognition using batlike sonar

Echolocating bats have excellent spatial memory and are able to navigate to salient locations using bio-sonar. Navigating and route-following require animals to recognize places. Currently, it is mostly unknown how bats recognize places using echolocation. In this paper, we propose template based place recognition might underlie sonar-based navigation in bats. Under this hypothesis, bats recognize places by remembering their echo signature - rather than their 3D layout. Using a large body of ensonification data collected in three different habitats, we test the viability of this hypothesis assessing two critical properties of the proposed echo signatures: (1) they can be uniquely classified and (2) they vary continuously across space. Based on the results presented, we conclude that the proposed echo signatures satisfy both criteria. We discuss how these two properties of the echo signatures can support navigation and building a cognitive map.