论文标题
开发模块化和潜水的软机器人臂以及相应的学习运动学模型
Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models
论文作者
论文摘要
自然界中发现的许多软体体生物在水下蓬勃发展。同样，软机器人可能非常适合水下环境，部分原因是重力，摩擦和谐波振荡的问题影响不太严重。但是，设计，制造，防水，模型和控制水下软机器人系统仍然是一个挑战。此外，由于需要密封的电子和机械元素，因此潜水机器人通常没有可配置的组件。这项工作介绍了由液压执行器驱动的模块化和潜水软机器人臂的开发，该动力臂由大多数可打印的零件组成，可以在相对较短的时间内组装或修改。它的模块化设计可实现多种形状配置和轻松交换软执行器。作为探索该系统上机器学习控制算法的第一步，我们还提出了使用深神经网络开发的初步前进和逆运动学模型。
Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm driven by hydraulic actuators which consists of mostly 3D printable parts which can be assembled or modified in a relatively short amount of time. Its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this system, we also present preliminary forward and inverse kinematics models developed using deep neural networks.
