A fully autonomous terrestrial bat-like acoustic robot

Eliakim, Itamar; Cohen, Zahi; Kósa, Gábor; Yovel, Yossi

doi:10.1371/journal.pcbi.1006406

Cited by 57 publications

(35 citation statements)

References 39 publications

(46 reference statements)

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“…In contrast to the work of Yamada et al [48] and Eliakim et al [49], our approach does not assume that obstacles can be localized. Therefore it is of particular interest that the environments in which Yamada et al [48] evaluated their robot were similar to the rectangular arena used in this paper.…”

Section: Avoidance Of Non-localized Obstaclesmentioning

confidence: 94%

“…Previous work on robot models of bat obstacle behavior has assumed that the bat can (approximately) localize obstacles. Both Yamada et al [48] and Eliakim et al [49] presented robot-based models in which obstacle location is used as input to an obstacle avoidance algorithm. Both studies estimated the azimuth location of obstacles using interaural time differences, a cue that might not be available to bats to localize individual reflectors [50,51].…”

Section: Avoidance Of Non-localized Obstaclesmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Avoidance Of Non-localized Obstaclesmentioning

confidence: 94%

Section: Avoidance Of Non-localized Obstaclesmentioning

confidence: 99%

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

et al. 2019

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“…Since standard cameras and LIDAR do not work well in these environments, sonar is a reasonable alternative or complementary sensor. Sonar has been shown to be useful for obstacle avoidance (Eliakim et al, 2018 ). Currently, the most common use of sonar systems is underwater.…”

Section: Introductionmentioning

confidence: 99%

Echo View Cells From Bio-Inspired Sonar

Isbell

Horiuchi

2020

Front. Neurorobot.

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Place recognition is naturally informed by the mosaic of sensations we remember from previously visiting a location and general knowledge of our location in the world. Neurons in the mammalian brain (specifically in the hippocampus formation) named "place cells" are thought to reflect this recognition of place and are involved in implementing a spatial map that can be used for path planning and memory recall. In this research, we use bat-inspired sonar to mimic how bats might sense objects in the environment and recognize the views associated with different places. These "echo view cells" may contribute (along with odometry) to the creation of place cell representations observed in bats. Although detailed sensory template matching is straightforward, it is quite unlikely that a flying animal or robot will return to the exact 3-D position and pose where the original memory was captured. Instead, we strive to recognize views over extended regions that are many body lengths in size, reducing the number of places to be remembered for a map. We have successfully demonstrated some of this spatial invariance by training feed-forward neural networks (traditional neural networks and spiking neural networks) to recognize 66 distinct places in a laboratory environment over a limited range of translations and rotations. We further show how the echo view cells respond between known views and how their outputs can be combined over time for continuity.

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“…Eliakim et al also used broadband chirps for robot navigation. They utilized generic features from audio processing for binary obstacle classification (“plant/no plant” [ 22 ]).…”

Section: Introductionmentioning

confidence: 99%

Classification of Sonar Targets in Air: A Neural Network Approach

Kroh

Simon

Rupitsch

2019

Sensors

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Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be exploited to identify and classify sound-reflecting objects (targets), which may then be used as landmarks for navigation. In the presented work, sonar targets of different geometric shapes and sizes are classified with custom-engineered features. Artificial neural networks (ANNs) with multiple hidden layers are applied as classifiers and different features are tested as well as compared. We concentrate on features that are related to target strength estimates derived from pulse-compressed echoes. In doing so, one is able to distinguish different target geometries with a high rate of success and to perform tests with ANNs regarding their capabilities for size discrimination of targets with the same geometric shape. A comparison of achievable classifier performance with wideband and narrowband chirp excitation signals was conducted as well. The research indicates that our engineered features and excitation signals are suitable for the target classification task.

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A fully autonomous terrestrial bat-like acoustic robot

Cited by 57 publications

References 39 publications

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

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

Echo View Cells From Bio-Inspired Sonar

Classification of Sonar Targets in Air: A Neural Network Approach

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