Oikonomou Paris, Dometios Athanasios, Khamassi Mehdi, Tzafestas Costas S
Division of Signals, Control and Robotics, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Sorbonne Université, Centre National de la Recherche Scientifique, Institute of Intelligent Systems and Robotics, Paris, France.
Front Robot AI. 2023 Oct 19;10:1256763. doi: 10.3389/frobt.2023.1256763. eCollection 2023.
In recent years, soft robots gain increasing attention as a result of their compliance when operating in unstructured environments, and their flexibility that ensures safety when interacting with humans. However, challenges lie on the difficulty to develop control algorithms due to various limitations induced by their soft structure. In this paper, we introduce a novel technique that aims to perform motion control of a modular bio-inspired soft-robotic arm, with the main focus lying on facilitating the qualitative reproduction of well-specified periodic trajectories. The introduced method combines the notion behind two previously developed methodologies both based on the Movement Primitive (MP) theory, by exploiting their capabilities while coping with their main drawbacks. Concretely, the requested actuation is initially computed using a Probabilistic MP (ProMP)-based method that considers the trajectory as a combination of simple movements previously learned and stored as a MP library. Subsequently, the key components of the resulting actuation are extracted and filtered in the frequency domain. These are eventually used as input to a Central Pattern Generator (CPG)-based model that takes over the generation of rhythmic patterns at the motor level. The proposed methodology is evaluated on a two-module soft arm. Results show that the first algorithmic component (ProMP) provides an immediate estimation of the requested actuation by avoiding time-consuming training, while the latter (CPG) further simplifies the execution by allowing its control through a low-dimensional parameterization. Altogether, these results open new avenues for the rapid acquisition of periodic movements in soft robots, and their compression into CPG parameters for long-term storage and execution.
近年来，软机器人因其在非结构化环境中操作时的柔顺性以及与人类交互时确保安全的灵活性而受到越来越多的关注。然而，由于其软结构带来的各种限制，开发控制算法存在困难。在本文中，我们介绍了一种新颖的技术，旨在对模块化生物启发式软机器人手臂进行运动控制，主要重点在于促进特定周期性轨迹的定性再现。所介绍的方法结合了两种先前基于运动原语（MP）理论开发的方法背后的概念，通过利用它们的能力同时应对其主要缺点。具体而言，首先使用基于概率运动原语（ProMP）的方法计算所需的驱动，该方法将轨迹视为先前学习并存储为MP库的简单运动的组合。随后，在频域中提取并过滤所得驱动的关键组件。这些最终用作基于中枢模式发生器（CPG）的模型的输入，该模型在运动层面接管节奏模式的生成。所提出的方法在一个双模块软手臂上进行了评估。结果表明，第一个算法组件（ProMP）通过避免耗时的训练提供了对所需驱动的即时估计，而后者（CPG）通过允许通过低维参数化进行控制进一步简化了执行。总之，这些结果为软机器人中周期性运动的快速获取以及将其压缩为CPG参数以进行长期存储和执行开辟了新途径。
