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Echo View Cells From Bio-Inspired Sonar.

Jacob D Isbell¹, Timothy K Horiuchi^1,2,3

¹Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States.

Frontiers in Neurorobotics

|November 30, 2020

Summary

This summary is machine-generated.

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This study introduces echo view cells, inspired by bat sonar, to improve place recognition in robots and animals. These cells help create spatial maps by recognizing places over larger areas, reducing memory needs.

Area of Science:

Neuroscience
Robotics
Artificial Intelligence

Background:

Place recognition relies on sensory memory and general spatial knowledge.
Hippocampus place cells are crucial for spatial mapping, path planning, and memory recall in mammals.
Robots and flying animals need robust place recognition that is invariant to exact position.

Purpose of the Study:

To investigate bat-inspired sonar for environmental sensing and place recognition.
To develop 'echo view cells' that contribute to spatial map creation, mimicking biological place cells.
To achieve place recognition over extended regions, reducing the number of places to be mapped.

Main Methods:

Utilized bat-inspired sonar to mimic echolocation for environmental sensing.

Keywords:

bat echolocation neural network place cells place fields robotics skim sonar

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Trained feed-forward neural networks, including traditional and spiking neural networks, for place recognition.

Tested the ability of echo view cells to recognize 66 distinct locations within a laboratory setting.

Main Results:

Demonstrated spatial invariance in place recognition across limited translations and rotations.
Successfully trained neural networks to identify numerous distinct places using sonar-based views.
Showcased how echo view cells respond between known locations and can be integrated over time for continuous mapping.

Conclusions:

Echo view cells offer a promising approach for robust place recognition in artificial systems.
This method can reduce the memory requirements for mapping large environments.
The findings contribute to understanding biological spatial cognition and developing advanced robotic navigation systems.