This paper proposes a novel framework for humanoid robots to execute inspection tasks with high efficiency and millimeter-level precision. The approach combines hierarchical planning, time-optimal standing position generation, and integrated \ac{mpc} to achieve high speed and precision. A hierarchical planning strategy, leveraging \ac{ik} and \ac{mip}, reduces computational complexity by decoupling the high-dimensional planning problem. A novel MIP formulation optimizes standing position selection and trajectory length, minimizing task completion time. Furthermore, an MPC system with simplified kinematics and single-step position correction ensures millimeter-level end-effector tracking accuracy. Validated through simulations and experiments on the Kuavo 4Pro humanoid platform, the framework demonstrates low time cost and a high success rate in multi-location tasks, enabling efficient and precise execution of complex industrial operations.

翻译：本文提出了一种新颖的框架，旨在使人形机器人能够以高效率和毫米级精度执行检测任务。该方法结合了分层规划、时间最优站立位置生成以及集成的模型预测控制，以实现高速与高精度。一种利用逆运动学和混合整数规划的分层规划策略，通过解耦高维规划问题来降低计算复杂度。一种新颖的混合整数规划模型优化了站立位置选择和轨迹长度，从而最小化了任务完成时间。此外，一个采用简化运动学和单步位置校正的模型预测控制系统确保了末端执行器达到毫米级的跟踪精度。通过在Kuavo 4Pro人形机器人平台上进行的仿真与实验验证，该框架在多位置任务中表现出较低的时间成本和较高的成功率，从而能够高效且精确地执行复杂的工业操作。