Date of Award

2024

Document Type

Open Access Master's Report

Degree Name

Master of Science in Electrical and Computer Engineering (MS)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Hongyu An

Committee Member 1

Tan Chen

Committee Member 2

Christopher Kit Cischke

Abstract

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation rule.

We employ Gazebo to simulate an interactive environment and ROS (Robot Operating System) to manage UGV control and sensor communication, allowing real-time response and modular integration. The neuromorphic model mirrors brain-like associative memory, where only essential associations are stored, optimizing data handling, storage, and power consumption. By recreating fear conditioning, the UGV "learns" to avoid or move away from the simulated "danger" (red color), achieving efficient and adaptive behavior. This approach conserves computational power, memory, energy, and data as it reduces processing demands, helping real-time and low-power operations. This biologically inspired framework proves how fear conditioning principles can enhance UGV adaptability, response speed, and energy efficiency in dynamic and resource-limited environments.

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