提出分层任务模型预测控制，让机械臂更高效完成连续操作。
Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks
- 分层优化策略利用机器人冗余度，动态协调多任务优先级。
- 任务变化时轨迹跟踪性能提升42%，执行速度比单任务架构快2.3倍。
- 适用于需要连续动作的复杂服务场景，如配送与协作任务。
移动操作臂有望在日常生活中承担更复杂的任务。随着大语言模型的发展，任务规划器能更好将人类口语指令转化为任务序列。但依然缺乏一种能与高层任务规划无缝衔接、高效执行序列任务的决策算法。本文基于非线性词典序优化思想，提出一种新型分层任务模型预测控制框架，通过有效利用机器人冗余度，显著提升任务执行性能与响应速度。相较于最先进的任务优先逆运动学控制方法，本方法在任务变更、机器人奇异性及参考变化情况下，平均提升分层轨迹跟踪性能42%。相比典型单任务架构，所提分层控制架构使任务空间路径更短，执行时间缩短至2.3倍。实验在一台9自由度移动操作臂上验证了效果。
原文摘要 · Abstract (English)
Mobile manipulators are envisioned to serve more complex roles in people's everyday lives. With recent breakthroughs in large language models, task planners have become better at translating human verbal instructions into a sequence of tasks. However, there is still a need for a decision-making algorithm that can seamlessly interface with the high-level task planner to carry out the sequence of tasks efficiently. In this work, building on the idea of nonlinear lexicographic optimization, we propose a novel Hierarchical-Task Model Predictive Control framework that is able to complete sequential tasks with improved performance and reactivity by effectively leveraging the robot's redundancy. Compared to the state-of-the-art task-prioritized inverse kinematic control method, our approach has improved hierarchical trajectory tracking performance by 42% on average when facing task changes, robot singularity and reference variations. Compared to a typical single-task architecture, our proposed hierarchical task control architecture enables the robot to traverse a shorter path in task space and achieves an execution time 2.3 times faster when executing a sequence of delivery tasks. We demonstrated the results with real-world experiments on a 9 degrees of freedom mobile manipulator.
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