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Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space
Byungjoon Bae1, Doeon Lee1, Minseong Park1
1Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, USA.
Science Robotics
|May 15, 2024
Summary
Researchers mimicked praying mantis vision to create artificial compound eyes for 3D object tracking. This biomimetic imager offers energy-efficient, accurate spatiotemporal sensing and tracking with low latency.
Area of Science:
- Biomimetic engineering
- Robotics and computer vision
- Neuroscience and sensory systems
Background:
- Arthropod eyes excel at object tracking and wide field of view but lack depth perception for static objects.
- Most arthropods use motion parallax for 3D object tracking, unlike mammals.
- Praying mantises uniquely combine compound eyes and stereopsis for 3D object recognition.
Purpose of the Study:
- To mimic the praying mantis vision system using artificial compound eyes for 3D spatiotemporal object sensing and tracking.
- To develop an energy-efficient system with minimal latency for visual information processing.
- To explore hardware and software co-design for edge computing and sensing capabilities.
Main Methods:
- Designed and fabricated stereoscopically coupled artificial compound eyes inspired by praying mantis vision.
- Implemented edge processing using a synaptic device and a federated split learning algorithm.
- Evaluated spatiotemporal object sensing and optical flow tracking accuracy and energy consumption.
Main Results:
- The biomimetic imager achieved accurate spatiotemporal object sensing and tracking in 3D space with a wide field of view.
- The system demonstrated a root mean square error of 0.3 cm for tracking.
- Energy consumption was approximately 4 millijoules, over 400 times lower than conventional CMOS imaging systems.
Conclusions:
- The stereoscopic artificial compound eye effectively replicates praying mantis vision for 3D object sensing and tracking.
- Edge processing with synaptic devices and federated learning enables energy-efficient, low-latency visual sensing.
- This biomimetic approach highlights the potential of integrating natural designs with co-designed technology for advanced edge computing and sensing.
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