A Cable-Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-08-29 DOI:10.1002/aisy.202400112
Kunyu Zhou, Baijin Mao, Yuzhu Zhang, Yaozhen Chen, Yuyaocen Xiang, Zhenping Yu, Hongwei Hao, Wei Tang, Yanwen Li, Houde Liu, Xueqian Wang, Xiaohao Wang, Juntian Qu
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Abstract

The growing interest in the flexibility and operational capabilities of soft manipulators in confined spaces emphasizes the need for precise modeling and accurate motion control. Conventional control methods encounter difficulties in modeling and involve intricate computations. This work introduces a novel deep reinforcement learning (DRL) control algorithm based on neural network modeling. Using the Whale Optimization Algorithm, an approximate dynamic model for the soft manipulator is established. The twin delayed deterministic policy gradient is employed for DRL control. Domain randomization is applied during pretraining in a simulated environment. The algorithm addresses issues related to dependency on measurement data quality and redundant mappings, outperforming other methods by 8–15 mm in control accuracy. The trained DRL controller achieves precise trajectory tracking within the soft manipulator's task space, enabling successful grasping tasks in various complex environments, including pipelines and other narrow spaces. Experimental results confirm the autonomy of our controller in performing these tasks without human intervention.

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基于深度强化学习的用于灵巧抓取的线控软机械手
人们对软体机械手在狭小空间内的灵活性和操作能力越来越感兴趣,这就强调了精确建模和精确运动控制的必要性。传统的控制方法在建模方面存在困难,并且涉及复杂的计算。这项工作介绍了一种基于神经网络建模的新型深度强化学习(DRL)控制算法。利用鲸鱼优化算法,建立了软机械手的近似动态模型。双延迟确定性策略梯度被用于 DRL 控制。在模拟环境中进行预训练时,采用域随机化。该算法解决了与测量数据质量和冗余映射相关的问题,控制精度比其他方法高出 8-15 毫米。训练有素的 DRL 控制器可在软机械手的任务空间内实现精确的轨迹跟踪,从而在各种复杂环境(包括管道和其他狭窄空间)中成功完成抓取任务。实验结果证实,我们的控制器能够在没有人工干预的情况下自主执行这些任务。
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审稿时长
4 weeks
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