联合预测形变与张力，实现柔顺线性物体的安全操控
Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction
- 同时预测物体形状和张力，确保接触与受力安全
- 在7自由度机械臂上完成线束装配，成功率更高且无安全违规
- 适合需要高安全性的工业自动化场景
操控柔顺线性物体（DLOs）因复杂动力学和接触环境中的安全性要求而极具挑战。现有方法多仅关注形状预测，忽略接触与张力约束，易导致物体或机器人损坏。本文提出一种可证明安全的运动规划与控制框架，核心为联合预测未来形状与张力的模型。该预测结果被集成至基于多项式区间体的实时轨迹优化器中，实现执行过程中的全程安全约束。我们在7自由度机械臂上模拟线束装配任务进行评估，相比先进方法，本方案任务成功率更高，且未发生任何安全违规。结果表明，该方法能有效支持接触密集环境下的鲁棒、安全的DLO操控。
原文摘要 · Abstract (English)
Manipulating deformable linear objects (DLOs) is challenging due to their complex dynamics and the need for safe interaction in contact-rich environments. Most existing models focus on shape prediction alone and fail to account for contact and tension constraints, which can lead to damage to both the DLO and the robot. In this work, we propose a certifiably safe motion planning and control framework for DLO manipulation. At the core of our method is a predictive model that jointly estimates the DLO's future shape and tension. These predictions are integrated into a real-time trajectory optimizer based on polynomial zonotopes, allowing us to enforce safety constraints throughout the execution. We evaluate our framework on a simulated wire harness assembly task using a 7-DOF robotic arm. Compared to state-of-the-art methods, our approach achieves a higher task success rate while avoiding all safety violations. The results demonstrate that our method enables robust and safe DLO manipulation in contact-rich environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。