Neuromorphic Processing and Bio-Inspired Navigation in Unstructured Environments

Autonomous navigation in complex, dynamic, and unstructured environments remains a fundamental challenge in field robotics. Traditional algorithmic paradigms often suffer from high computational overhead, latency, and fragility when exposed to unpredictable sensory conditions or hardware constraints. In contrast, biological organisms, ranging from individual foraging insects to coordinated animal swarms, demonstrate remarkable efficiency, adaptability, and resilience using sparse, noisy sensory inputs and highly optimized neural architectures. Bridging the gap between biological mechanisms and robotic systems offers a promising pathway toward next-generation autonomous platforms equipped with low-latency sensing and energy-efficient computation.This Research Topic aims to explore innovative computational frameworks, bio-inspired control strategies, and neuromorphic architecture implementations designed to enhance robotic autonomy and navigation in real-world, dynamic scenarios. By integrating principles from neuromorphic processing with bio-inspired swarm intelligence and adaptive multi-sensor fusion, this collection seeks to overcome current bottlenecks in energy efficiency, spatial awareness, and real-time decision-making. We aim to bring together leading researchers from robotics, neuroscience, neuromorphic engineering, and artificial intelligence to highlight state-of-the-art advances, benchmark current approaches, and define future directions for resilient autonomous systems.We welcome Original Research, Reviews, Mini-Reviews, Brief Research Reports, and Perspective articles addressing key technical and conceptual challenges in bio-inspired navigation and neuromorphic computing. Topic editors: Weimin Zhang, Marco Ceccarelli Source: https://www.frontiersin.org/research-topics/84107/neuromorphic-processing-and-bio-inspired-navigation-in-unstructured-environments