A Hybrid Control Architecture for Autonomous Robotic Fish

Abstract

This paper presents a hybrid control architecture for autonomous robotic fishes which are able to swim and navigate in unknown or dynamically changing environments. It has a three-layer configuration: cognitive layer, behaviour layer and swim pattern layer. The state-based planning in the cognitive layer provides a good foundation for potential adaptation through machine learning methods such as reinforcement learning (RL). The behaviour layer and the swim pattern layer are specially designed to match the needs for the real-time control of our robotic fish. To test the feasibility and performance of the proposed architecture, the experiment of "tank border exploration" is conducted with Q-learning