The proposed navigation algorithm addresses key limitations of traditional neural networks—mainly their lack of generalizability and real-world adaptability—by leveraging a modular dual-network design. One network learns from odometry for space perception, while the other focuses on dynamic pathfinding via RBF and VFH integration, improving obstacle avoidance and real-time decision-making. This approach enhances interpretability, reduces overfitting to specific sensor types, and ensures scalability across robotic platforms. In our research, a similar philosophy was applied using hybrid architectures (e.g., DQL + AGTO) to improve path realism in dynamic environments like cybersecurity and robotic vision systems.

Related Work:

Deep Guard: Fortifying Digital Authenticity with Deep Q-Learning and AGTO :- Article Deep Guard: Fortifying Digital Authenticity with Deep Q-Lear...

Retinal Twins: Leveraging Binocular Symmetry with Siamese Networks :- Article Retinal Twins Leveraging Binocular Symmetry with Siamese Net...