让人形机器人高效精准完成多点巡检任务
Path and Motion Optimization for Efficient Multi-Location Inspection with Humanoid Robots
- 分层规划结合逆运动学与混合整数规划，降低计算复杂度
- 优化站立位置与路径长度，任务完成时间显著减少
- 单步位置修正的模型预测控制实现毫米级精度
本文提出一种新型框架，使人形机器人能以高效率和毫米级精度执行巡检任务。该方法融合分层规划、时间最优站立位生成与集成模型预测控制（MPC），实现高速与高精度。通过逆运动学（IK）与混合整数规划（MIP）解耦高维规划问题，降低计算复杂度；提出新颖的MIP公式，优化站立位选择与轨迹长度，最小化任务完成时间；同时，采用简化运动学与单步位置修正的MPC系统，确保末端执行器达到毫米级跟踪精度。在Kuavo 4Pro人形平台的仿真与实验验证中，该框架展现出低时间成本与高成功率，可高效完成复杂的工业巡检操作。
原文摘要 · Abstract (English)
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.
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