Multi-objective evolutionary design of central pattern generator network for biomimetic robotic fish

Abstract

AbstractFish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model’s effectiveness and good performance, especially when the control model has been applied to the real biomimetic robotic fish.