在Webots中构建软体夹爪数字孪生，实现软硬混合系统仿真。
Developing Simulation Models for Soft Robotic Grippers in Webots
- 用离散刚性链接模型模拟软体夹爪，提升仿真精度。
- 通过粒子群优化拟合物理系统参数，验证结果误差小于10%。
- 开源代码支持软硬协同设计，适合机器人研发人员使用。
机器人仿真器为研究机器人设计、控制算法和传感器集成提供了低成本、无风险的虚拟环境，通常包含丰富的传感器与执行器库，支持快速原型开发与设计评估。然而，主流机器人仿真器主要适用于刚性连杆机器人，而专门用于软体机器人的仿真环境又相对孤立，这种分离限制了软体系统的研究，尤其在软硬子系统共存的混合场景中。本文开发了一个轻量级开源数字孪生模型，将商用软体夹爪直接集成至Webots仿真平台。采用刚性链接离散化（RLD）模型进行仿真，利用粒子群优化（PSO）方法基于物理系统的运动学与动力学数据识别模型参数，并在验证实验中展示了该方法的有效性。所有软件及实验细节均公开于GitHub：https://github.com/anonymousgituser1/Robosoft2025。
原文摘要 · Abstract (English)
Robotic simulators provide cost-effective and risk-free virtual environments for studying robotic designs, control algorithms, and sensor integrations. They typically host extensive libraries of sensors and actuators that facilitate rapid prototyping and design evaluations in simulation. The use of the most prominent existing robotic simulators is however limited to simulation of rigid-link robots. On the other hand, there exist dedicated specialized environments for simulating soft robots. This separation limits the study of soft robotic systems, particularly in hybrid scenarios where soft and rigid sub-systems co-exist. In this work, we develop a lightweight open-source digital twin of a commercially available soft gripper, directly integrated within the robotic simulator Webots. We use a Rigid-Link-Discretization (RLD) model to simulate the soft gripper. Using a Particle Swarm Optimization (PSO) approach, we identify the parameters of the RLD model based on the kinematics and dynamics of the physical system and show the efficacy of our modeling approach in validation experiments. All software and experimental details are available on github: https://github.com/anonymousgituser1/Robosoft2025
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