From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking

• URL: http://arxiv.org/abs/2602.05683v1
• Date: Thu, 05 Feb 2026 14:09:09 GMT
• Title: From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
• Abstract summary: We propose a parsimonious neuromorphic control framework for vision-guided navigation and tracking.<n>Image pixels from an onboard camera are encoded as inputs to dynamic neuronal populations.<n>Inspired by recently proposed mechanistic models of animal cognition and opinion dynamics, the neuromorphic controller provides real-time autonomy with a minimal computational burden.
• Score: 10.144707192962068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic navigation has historically struggled to reconcile reactive, sensor-based control with the decisive capabilities of model-based planners. This duality becomes critical when the absence of a predominant option among goals leads to indecision, challenging reactive systems to break symmetries without computationally-intense planners. We propose a parsimonious neuromorphic control framework that bridges this gap for vision-guided navigation and tracking. Image pixels from an onboard camera are encoded as inputs to dynamic neuronal populations that directly transform visual target excitation into egocentric motion commands. A dynamic bifurcation mechanism resolves indecision by delaying commitment until a critical point induced by the environmental geometry. Inspired by recently proposed mechanistic models of animal cognition and opinion dynamics, the neuromorphic controller provides real-time autonomy with a minimal computational burden, a small number of interpretable parameters, and can be seamlessly integrated with application-specific image processing pipelines. We validate our approach in simulation environments as well as on an experimental quadrotor platform.

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