面向序列全身移动操作的动态规划
Dynamic Planning for Sequential Whole-body Mobile Manipulation
AI总结：
针对现有移动操作动态规划方法的局限性，研究提出融合在线任务规划、VKC全身运动规划与反应式MPC的一体化框架，经仿真与真实实验验证可在含人为干扰的动态场景下高效完成序列移动操作任务。
摘要：
动态序列移动操作规划（SMMP）框架是移动操作器在动态环境中安全、鲁棒运行的核心。以往研究大多聚焦于运动层或任务层的动态规划，前者难以处理具备长期影响的状态变化，后者则无法为多样化任务生成响应式运动。本文提出一种一体化动态规划框架，对基于虚拟运动链（VKC）的SMMP方法进行扩展，可自动完成SMMP问题的动态长期任务规划与反应式全身运动生成。该框架包含三个模块：用于响应具有长期影响的环境变化的在线任务规划模块、可操作刚性物体与铰接物体的基于VKC的全身运动规划模块，以及执行阶段用于避障的反应式模型预测控制（MPC）模块。仿真与真实世界实验验证了该框架的有效性，证明其在序列移动操作任务中具备良好的效能与可行性，即便在存在人为干扰的场景下也能正常工作。
英文摘要：
The dynamic Sequential Mobile Manipulation Planning (SMMP) framework is essential for the safe and robust operation of mobile manipulators in dynamic environments. Previous research has primarily focused on either motion-level or task-level dynamic planning, with limitations in handling state changes that have long-term effects or in generating responsive motions for diverse tasks, respectively. This paper presents a holistic dynamic planning framework that extends the Virtual Kinematic Chain (VKC)-based SMMP method, automating dynamic long-term task planning and reactive whole-body motion generation for SMMP problems. The framework consists of an online task planning module designed to respond to environment changes with long-term effects, a VKC-based whole-body motion planning module for manipulating both rigid and articulated objects, alongside a reactive Model Predictive Control (MPC) module for obstacle avoidance during execution. Simulations and real-world experiments validate the framework, demonstrating its efficacy and validity across sequential mobile manipulation tasks, even in scenarios involving human interference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
