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.
翻译:同样,软机器人可能适合水下环境,部分是因为重力、摩擦和声波交错产生的问题效应在水下较不严重。然而,设计、制造、防水、模型和控制水下软机器人系统仍是一项挑战。此外,潜水机器人通常没有可配置的部件,因为需要密封的电子和机械元件。这项工作展示了由液压驱动器驱动的模块和可潜水软机器人臂的开发,该机体主要由三维可打印部件组成,可以在相对较短的时间内组装或改装。其模块设计使得多形状配置和容易交换软动画器。作为探索系统机器学习控制算法的第一步,我们还展示了利用深神经网络开发的初步前向和反向运动模型。