Intelligent Control Strategy for Robotic Manta via CPG and Deep Reinforcement Learning

Author:

Su Shijie1ORCID,Chen Yushuo1ORCID,Li Cunjun2,Ni Kai1,Zhang Jian1ORCID

Affiliation:

1. College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China

2. Zhoushan Institute of Calibration and Testing for Quality and Technology Supervision, Zhoushan 316021, China

Abstract

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.

Funder

the Key Research Projects on Basic Sciences (Natural Sciences) in Higher Education Institutions of Jiangsu Province of China

Publisher

MDPI AG

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