基于学习的椎骨软体机器人尾巴压力预测控制
Learning-Based Pressure Predictive Control of a Vertebraic Soft Robotic Tail
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中文总结 AI 辅助
本文提出基于LSTM的压力预测控制（PPC）方法，用于椎骨软体机器人尾巴的运动控制及与四足机器人协调，通过IK、FK和压力补偿模型实现非静态与准静态控制，显著降低轨迹误差并提升实时性。
中文摘要 AI 辅助
软体机器人因其安全的人机交互和灵活性而备受关注，但典型的连续体结构和非线性材料行为使得运动学建模复杂，尤其是在非静态运动中。在这项工作中，我们提出了一种基于LSTM的压力预测控制（PPC），用于椎骨软体机器人尾巴的运动控制以及与四足机器人的协调。PPC由逆运动学（IK）模型、正运动学（FK）模型和压力补偿（P-comp）模型组成，实现了尾巴的非静态和准静态运动控制。与仅使用IK模型相比，在执行目标轨迹时，PPC的仿真轨迹的平均均方根误差（RMSE）降低了69.8%。在软体尾巴四足机器人的协调运动中，使用预测数据集训练PPC能够实现下一时刻的动作预测，并将计算时间减少了60.9%，从而增强了尾巴的实时响应能力，以匹配四足机器人躯干的移动速率。PPC提供了一种简单有效的方法来建模软体尾巴，用于非静态和准静态运动控制，并赋予软体尾巴四足机器人与环境交互的功能。
英文摘要
Soft robots have attracted much attention for their safe human-robot interaction and flexibility, but the typical continuum structure and nonlinear material behavior make the kinematics modelling complex, especially in non-static motions. In this work, we proposed an LSTM-based pressure predictive control (PPC) for the motion control of a vertebraic soft robotic tail and the coordination with a quadruped robot. The PPC consists of an inverse kinematics (IK) model, a forward kinematics (FK) model and a pressure compensation (P-comp) model, and achieves non-static and quasi-static motion control of the tail. Compared with the IK-only model, the average RMSE of the PPC's simulation trajectories reduces by 69.8%, when executing target trajectories. In the coordinated motions of the soft tail quadruped, using a prediction data set to train the PPC enables next-moment action prediction and reduces computation time by 60.9%, which enhances the real-time response of the tail to match the quadruped torso's moving rate. The PPC provides a simple and effective method to model the soft tail for both non-static and quasi-static motion control, and grants the soft tail quadruped with the functionality of interacting with the environment.
