From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
🤖 AI Summary
This work proposes a lightweight neuromorphic control framework to address the decision deadlock caused by symmetry in robotic systems lacking a dominant goal, which often struggle to balance reactivity and deliberative planning. By directly encoding pixels from an onboard camera into dynamic neuronal population inputs and integrating a bifurcation mechanism inspired by animal cognition, the approach defers commitment until critical decision points, enabling end-to-end mapping from visual perception to egocentric motor commands. The framework synergistically combines neuromorphic computing, dynamical systems theory, and embedded vision processing to achieve interpretable, real-time autonomous decision-making with minimal computational overhead. Both simulated and real-world quadrotor experiments demonstrate its efficacy and robustness in autonomous navigation and target tracking tasks.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
robotic navigation
decision-making
indecision
reactive control
autonomous tracking
Innovation
Methods, ideas, or system contributions that make the work stand out.
neuromorphic control
dynamic bifurcation
vision-based navigation
decision-making under uncertainty
egocentric motion commands
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