Biologically inspired collision avoidance system for unmanned vehicles

Fernando E. Ortiz; Brett Graham; Kyle Spagnoli; Eric J. Kelmelis

doi:10.1117/12.820756

30 April 2009 Biologically inspired collision avoidance system for unmanned vehicles

Fernando E. Ortiz, Brett Graham, Kyle Spagnoli, Eric J. Kelmelis

Proceedings Volume 7332, Unmanned Systems Technology XI; 73320B (2009) https://doi.org/10.1117/12.820756
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

Citation Download Citation

Fernando E. Ortiz, Brett Graham, Kyle Spagnoli, and Eric J. Kelmelis "Biologically inspired collision avoidance system for unmanned vehicles", Proc. SPIE 7332, Unmanned Systems Technology XI, 73320B (30 April 2009); https://doi.org/10.1117/12.820756

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KEYWORDS

Unmanned aerial vehicles

Field programmable gate arrays

Visualization

Collision avoidance

Visual process modeling

3D modeling

Motion models

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