Marine-Inspired Multimodal Sensor Fusion and Neuromorphic Processing for Autonomous Navigation in Unstructured
Chandan Sheikder1,2, Weimin Zhang1,2, Xiaopeng Chen1,2
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 13, 2025
Summary
This study introduces a novel neuromorphic framework for resilient robotic navigation, inspired by marine species. It enables energy-efficient, real-time path integration and cognitive mapping in challenging underwater environments.
Area of Science:
- Robotics and Bio-inspired Engineering
- Neuroscience and Sensor Technology
Background:
- Autonomous navigation in GPS-denied environments faces challenges from sensor degradation and computational inefficiency.
- Existing robotic systems lack dynamic, context-aware multisensory fusion, leading to susceptibility to failures and high power consumption.
Purpose of the Study:
- To develop a novel neuromorphic framework for resilient robotic navigation in unstructured, GPS-denied environments.
- To bridge the gap in dynamic, context-aware multisensory fusion found in current robotic systems.
Main Methods:
- Co-design of marine-inspired sensors (replicating cephalopod neural processing and marine fauna quantum mechanisms) with an event-based neuromorphic processing backbone.
- Development of a hybrid sensor fusion model for real-time, energy-efficient path integration and cognitive mapping.
Main Results:
- The proposed framework enables real-time, energy-efficient navigation without reliance on traditional methods.
- Demonstrated potential for enhanced navigational robustness by overcoming limitations of state-of-the-art solutions.
Conclusions:
- Marine bio-inspired design offers a promising approach for advancing autonomous systems in critical underwater applications.
- The framework has potential for deep-sea exploration, environmental monitoring, and underwater infrastructure inspection.
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