Core Viewpoint - The article discusses the advancements in soft robotics, particularly modular soft robotic arms (MSRA), which address the limitations of traditional rigid robotic arms in complex, unstructured environments. The introduction of a bi-directional Long Short-Term Memory (biLSTM) network for intelligent control enhances the flexibility and adaptability of MSRA in various applications such as minimally invasive surgery and disaster rescue [1][2][22]. Group 1: Soft Robotics Development - Traditional rigid robotic arms dominate industrial automation due to their high precision and load capacity, but they face limitations in safety, environmental adaptability, and human-robot interaction in complex tasks [1]. - Soft robotics, based on biomimicry and flexible materials, offers unique advantages such as continuous deformation, high collision safety, and strong environmental compliance, opening new application dimensions [1]. Group 2: Modular Soft Robotic Arms (MSRA) - MSRA is a core research direction that decomposes robotic arms into standardized, interchangeable functional units, providing reconfigurability, scalability, and ease of maintenance [1]. - Existing MSRA technologies face challenges such as nonlinearity, time delays, and hysteresis, which limit their practical application [1]. Group 3: Intelligent Control Architecture - Researchers from Italy and Switzerland developed a biLSTM-based intelligent control architecture to solve the multi-functional collaborative control problem of MSRA, significantly enhancing its flexibility and situational awareness [2][22]. - The proposed method allows MSRA to perform precise operations in complex environments, such as in minimally invasive surgeries and narrow space explorations [2]. Group 4: Experimental Validation - The research team designed a cable-driven MSRA consisting of three independent modules, each approximately 0.2 meters long, driven by motors and connected by cables to ensure module independence [8][9]. - The system employs optical tracking for real-time, high-precision capture of the robot's position, providing a critical hardware foundation for training and validating the neural network [11]. Group 5: Performance Advantages - Experimental results show that the proposed method outperforms traditional methods in trajectory tracking tasks, with significantly lower tracking errors [17]. - The method also demonstrates the ability to handle complex tasks, such as maintaining specific positions while moving other parts of the robotic arm [17]. - The system successfully achieved obstacle avoidance and target tracking in dynamic environments, showcasing its adaptability [19].

最上方点击蓝字"四足机器人研习社",右上方选"设为星标" 不错过好文推送,第一时间看干货文章 读书使人充实，讨论使人机智，笔记使人准确，读史使人明智，读诗使人灵秀，数学使人周密，科学使人深刻，伦理使人庄重，逻辑修辞使人善辩，凡 有所学，皆成性格。 ———— （英国）培根 本公众号的文章和资料和四足机器人相关，包括行业的经典教材、行业资料手册，同时会涉及到职业知识学习及思考、行业发展、学习方法等一些方面 的文章。 本文目录 |0.前言 开源的四足控制框架中，对于小型的四足，一般采用舵机进行驱动，运动学层次的控制就能满足需求，对于稍大的四足来说，为了提升机器人的响应速度和 稳定性，就需要考虑其动力学影响，进行力矩（电流环）层次的控制。 足式机器人属于欠驱动约束动力学系统，与传统机械臂存在明显的差异： 1）动力学模型具有6 自由度欠驱动浮动机身； 2）动力学模型受到时变广义约束（足-地不连续接触）**的影响。 这两大因素 导致其逆动力学求解为典型的欠驱动不适定问题，因而使得多刚体逆动力学在四足机器人力控领域的应用受到了严重的制约。 四足机器人具有 典型的浮动机身，且存在不连续足-地交互问题，因而导致四足机器人动力学 ...