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Sound source localization is the ability to successfully understand the bearing and distance of a sound in space. The challenge of sound source localization has been a major are of research for engineers, especially those studying robotics, for decades. One of the main topics of focus is the ability for robots to track objects, human voices, or other robots robustly and accurately. Common ways to accomplish this goal may use large arrays, computationally intensive machine learning methods, or known dynamic models of a system which may not always be available. We seek to simplify this problem using a minimal amount of inexpensive equipment alongside a Bayesian estimator, capable of localizing an emitter using easily available a-priori information and timing data received from a prototype binaural sensor. We perform an experiment in a full anechoic chamber with a sound source moving at a constant speed; this experimental environment provides a space that allows us to isolate the performance of the sensor. We find that, while our current system isn’t perfect, it is able to track the general motion of a sound source and the path to even more accurate tracking in the future is clear.

 

Communication inspired by animals is a timely topic of research in the modeling and control of multi-agent systems. Examples of such bio-inspired communication methods include pheromone trails used by ants to forage for food and echolocation used by bats to orient themselves and hunt. Source searching is one of many challenges in the field of swarm robotics that tackles an analogous problem to animals foraging for food. This paper seeks to compare two communication methods, inspired by sonar and pheromones, in the context of a multi-agent foraging problem. We explore which model is more effective at recruiting agents to forage from a found target. The results of this work begin to uncover the complicated relationship between sensing modality, collective tasks, and spontaneous cooperation in groups.

 

In this work, we explore how the emergence of collective motion in a system of particles is influenced by the structure of their domain. Using the Vicsek model to generate flocking, we simulate two-dimensional systems that are confined based on varying obstacle arrangements. The presence of obstacles alters the topological structure of the domain where collective motion occurs, which, in turn, alters the scaling behavior. We evaluate these trends by considering the scaling exponent and critical noise threshold for the Vicsek model, as well as the associated diffusion properties of the system. We show that obstacles tend to inhibit collective motion by forcing particles to traverse the system based on curved trajectories that reflect the domain topology. Our results highlight key challenges related to the development of a more comprehensive understanding of geometric structure's influence on collective behavior. 

Ultrasonic bat detectors are useful for research and monitoring purposes to assess occupancy and relative activity of bat communities. Environmental “clutter” such as tree boles and foliage can affect the recording quality and identification of bat echolocation calls collected using ultrasonic detectors. It can also affect the transmission of calls and recognition by bats when using acoustic lure devices to attract bats to mist-nets. Bat detectors are often placed in forests, yet automatic identification programs are trained on call libraries using echolocation passes recorded largely from open spaces. Research indicates that using clutter-recorded calls can increase classification accuracy for some bat species and decrease accuracy for others, but a detailed understanding of how clutter impacts the recording and identification of echolocation calls remains elusive. To clarify this, we experimentally investigated how two measures of clutter (i.e., total basal area and number of stems of simulated woody growth, as well as recording angle) affected the recording and classification of a synthesized echolocation signal under controlled conditions in an anechoic chamber. Recording angle (i.e., receiver position relative to emitter) significantly influenced the probability of correct classification and differed significantly for many of the call parameters measured. The probability of recording echo pulses was also a function of clutter but only for the detector angle at 0° from the emitter that could receive deflected pulses. Overall, the two clutter metrics were overshadowed by proximity and angle of the receiver to the sound source but some deviations from the synthesized call in terms of maximum, minimum, and mean frequency parameters were observed. Results from our work may aid efforts to better understand underlying environmental conditions that produce false-positive and -negative identifications for bat species of interest and how this could be used to adjust survey accuracy estimates. Our results also help pave the way for future research into the development of acoustic lure technology by exploring the effects of environmental clutter on ultrasound transmission. 

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction. 

The problem of sound source localization has attracted the interest of researchers from different disciplines ranging from biology to robotics and navigation. It is in essence an estimation problem trying to estimate the location of the sound source using the information available to sound receivers. It is common practice to design Bayesian estimators based on a dynamic model of the system. Nevertheless, in some practical situations, such a dynamic model may not be available in the case of a moving sound source and instead, some a priori information about the sound source may be known. This paper considers a case study of designing an estimator using available a priori information, along with measurement signals received from a bearing-only sensor, to track a moving sound source in two dimensions. 

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles.