中国科学院自动化研究所机构知识库

This paper deals with the management and control of a biomimetic robotic fish within a control framework of artificial systems, computational experiments, and parallel execution (ACP). Without the need of precise hydrodynamic modeling and control implementation, we firstly built a unctionally equivalent artificial robotic fish by using the Agent technology. When performing a specific task, network-stored control strategies and environment models can be downloaded for computing, testing, and optimizing purposes. By parallel execution, the optimal algorithm can be transferred to the physical robotic fish, where error feedback signals serve to seek the optimal solution in the network. Furthermore, the optimized control strategies, environment models, as well as the newly learned knowledge will be uploaded to the network after accomplishing the mission. At last, we demonstrate this ACP-centered control method through pushball experiment on robotic fish.