Learning above-and-below relationship for vision-based robot navigation system using Distributed Hierarchical Graph Neuron (DHGN) algorithm

Obstacle avoidance is one of the important considerations in developing a vision-based robot navigation system. For flying robots, the ability to learn the above-and-below relationship for obstacle avoidance is necessary. This paper presents a conceptual work in developing a learning mechanism to identify the above-and-below relationship for obstacle avoidance in vision-based robot navigation system using a pattern recognition algorithm known as Distributed Hierarchical Graph Neuron (DHGN). DHGN is a bio-inspired pattern recognition algorithm that implements learning and memorization through a distributed and hierarchical processing. Preliminary results of simple above-and-below navigation with binary images using DHGN indicate that the scheme is able to produce high recall accuracy for obstacle detection. In addition, the proposed scheme implements a one-shot learning approach that is suitable for realtime deployment in robot navigation system.