基于力学的软体机器人运动模式、变刚度与传感技术研究

软体机器人是由软材料制备的机器人系统，能够凭借优异的柔顺性产生大幅连续变形，与周围环境产生密切接触而不造成破坏，特别适合执行人机安全交互以及非结构化环境中的任务，具有重要的研究价值。然而，软体机器人的应用与技术转化仍需要对其关键功能进行深入研究，软体机器人运动模式、变刚度以及磁传感技术的相关研究仍有不足。因此，本文以实验研究为核心研究方法，以力学建模为理论指导，辅以有限元仿真分析，针对软体机器人的运动模式、变刚度以及磁传感技术开展系统性研究及分析，主要工作包括如下三个方面：

（1）本文提出了线驱动软体机器人的模块化设计，通过单电机驱动两根驱动线产生的交替伸缩实现双向弯曲。推导了考虑轴向收缩的常曲率模型，并基于该模型和几何精确梁理论分别对线驱动软体机器人进行了运动学建模，并通过位姿测量实验验证了模型的准确性。进而，本文通过一系列实验，研究了具有不同模块组合软体机器人的运动模式及性能表现。实验结果表明，单模块软体爬行机器人凭借线驱动高效的力传递，具有优异的运动鲁棒性和适应性，能够在非结构化腔道环境内实现稳定的爬行运动，进而对软体爬行机器人的运动步态进行了重点分析，并进行了爬行过程的动力学仿真，说明了沿软体机器人长度方向上的不均匀机械特性是产生推进力的关键因素；双模块软体爬行机器人则能够实现腔道环境内的双向爬行；三模块蛇形软体机器人则能够通过各模块协作实现蛇形游水运动。该模块化设计的软体机器人具有稳定的运动能力以及多样化的运动模式，为移动式软体机器人的设计及应用提供了新思路。

（2）为保持软体机器人优异柔顺性的同时，增强其负载能力与稳定性，本文提出了一种融合材料相变和几何重构的复合变刚度原理。基于该原理，利用形状记忆聚合物和层阻塞技术，开发了一种复合变刚度结构。该结构既能通过焦耳热调节聚合物层的杨氏模量，也能通过负压驱动实现层阻塞，从而有效调节刚度并实现乘积变化效果。三点弯曲实验结果说明该复合变刚度结构在具有紧凑设计的同时，能够实现0.31 N/mm至4.86 N/mm，约15.7倍的刚度变化。激励响应实验结果则说明复合变刚度结构能够实现相对快速的刚度变化，层阻塞具有毫秒级的响应速度，而聚合物层的温升速率最高达2.77 °C/s。通过结合该复合变刚度结构与模块化软体机器人，本文开发了一种变刚度软体抓手。抓取实验说明该软体抓手保持了优异的柔顺性，能够有效抓取宽度为40 mm至190 mm不同尺寸的物体；其负载能力同样得到了显著增强，能够提升重达650 g的物体。该复合变刚度技术能够有效适配软体机器人系统，拓展了软体机器人在复杂环境下的应用范围。

（3）本文研究了磁传感技术在软体机器人触觉感知以及本体感知中的应用，开发了基于硬磁软材料的触觉传感器以及软体机器人，并随后开发了通用的算法来解决磁传感过程中的“正问题”与“反问题”。基于电磁学、连续介质力学以及有限元法开发了磁场分布算法。通过变形梯度张量以及网格等参数对微元磁矩进行精准建模进而计算磁场分布，解决磁传感过程中的“正问题”。同时，开发了以磁场变化为输入的深度学习模型，实现对各参数的非线性回归及多分类预测，解决磁传感过程中的“反问题”。随后进行了系统性的磁触觉传感实验，并训练了触觉感知模型，对位置预测的正确率为100 %，对力幅值预测的误差不超过6 %。本文同时计算了软体机器人在不同驱动条件下的磁场变化，并训练了本体感知模型，对形心线曲率半径预测的最大误差为4.1 %，对形心线弧长预测的最大误差为0.14 %。通过对软体机器人磁传感技术进行系统性的理论分析和实验验证，有效增强了软体机器人的感知能力，为软体机器人的应用提供了新的可能。

Soft robots, fabricated of soft materials with low elastic modulus, can perform substantial and continuous deformation due to the inherent superior compliance. Therefore, soft robots can contact with environments closely without causing damage, making them well-suited for tasks involving human-robot interaction and operations in unstructured environments. However, the research on locomotion, variable stiffness, and sensing in soft robotics is insufficient, the application of soft robots still needs in-depth and comprehensive research. Therefore, based on continuum mechanics and cooperating with finite element analysis (FEA), this thesis conducts a thorough theoretical and experimental study on the locomotion, variable stiffness, and magnetic sensing mechanism of soft robots. The main contributions of this thesis are delineated into three parts:

(1) This work investigates the modular design of tendon-driven soft robots and locomotion patterns. The minimally designed robot module is capable of bidirectional bending through the alternate contraction of two driven tendons actuated by a single motor. The kinematic modeling is performed by the constant curvature model considering axial contraction and the geometrically exact beam theory, which is validated through the configuration characterization tests. Subsequently, the locomotion and performance of soft robots with various modular designs are experimentally studied. The results demonstrate that with the efficient force transmission of tendon-driven mechanism, the single-modular soft crawling robot exhibits remarkable locomotion robustness and adaptability, enabling unidirectional crawling within unstructured pipes; the gait pattern and locomotion mechanism are experimentally investigated and analyzed by FEA, revealing that the forward frictional force is generated due to the asymmetric mechanical properties along the length direction of the robot. Additionally, dual-modular soft crawling robots are capable of bidirectional locomotion within pipes, and the snake-like soft robot with three modules can achieve serpentine swimming locomotion. The proposed modular design of soft robots could inspire novel design, locomotion, and application in unstructured environments.

(2) To enhance the stiffness and stability of soft robots without compromising their intrinsic compliance, this work introduces a novel Hybrid Variable Stiffness (HVS) mechanism that integrates material phase transition with geometric reconfiguration. Leveraging this mechanism, a pioneering HVS structure incorporating shape memory polymer (SMP) and layer jamming (LJ) is developed, which can modify Young's modulus via Joule heating, and achieving LJ through vacuum pressure, thereby achieving a multiplicative effect of stiffness change. Bending tests reveal that the compact bi-layer designed HVS structure can achieve a 15.7 times stiffness range, from 0.31 N/mm to 4.86 N/mm, which has been verified by FEA simulation. Response tests show that the jamming response is rapid while the maximum heating rate is 2.77 °C/s, indicating that the HVS structure can achieve a relatively fast response. Furthermore, building upon the HVS structure and the modular tendon-driven soft robots, a variable stiffness soft gripper with excellent compliance and load-bearing capability is developed. Several grasping tests indicate that the soft gripper could grasp various objects with diverse materials and shapes from 40.0 mm to 190 mm, 4.75 times, while lifting a weight up to 650 g. The proposed HVS mechanism provides an effective solution for soft robots to meet the intricate requirements of applications in unstructured environments.

(3) This work systematically investigates the magnetic sensing mechanism and its applications in the tactile and proprioceptive sensing of soft robots. A magnetic tactile sensor and soft robots are developed based on hard magnetic soft materials. Then, a universal algorithm is developed to solve the "forward problem" and "inverse problem" of the magnetic sensing process. Leveraging electromagnetism, continuum mechanics, and FEA simulation, a magnetic field distribution calculation algorithm is introduced based on the modeling of the magnetic moment of each element through deformation gradient tensors and mesh parameters. In parallel, with the magnetic field variations as input, a deep learning model capable of regression and classification, was developed to predict the tactile force and the configuration of soft robots. Then, comprehensive magnetic tactile sensing experiments are conducted. The proposed tactile perception model achieves a 100% accuracy rate in location prediction and a maximum error of 6% in force amplitude prediction. Additionally, the magnetic field change of a tendon-driven soft robot under diverse actuation conditions is calculated. The proprioception model achieves a maximum error of 4.1% in radius prediction and a maximum error of 0.14% in arc length prediction. The theoretical analysis and experimental study of magnetic sensing mechanism offer new possibilities for the enhanced sensing ability of soft robots.

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