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arXiv 2402.04730cs.RO

串行机械臂动态变化路点的模型预测轨迹优化

Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators

Technische Universität Wien (TUW)(维也纳技术大学);AIT Austrian Institute of Technology GmbH(奥地利技术研究院有限公司)

Florian Beck, Minh Nhat Vu, Christian Hartl-Nesic, Andreas Kugi

AI总结:

针对短时域在线轨迹优化难以纳入动态路点的问题,提出路点模型预测控制(wMPC),通过分割规划时域并缩短时域长度,实现低计算负载的实时重规划,实验验证其性能与离线RRT型规划器相当。

摘要:

在在线模型预测轨迹优化中,系统性地将动态变化的路点作为期望的离散动作(例如由上级任务规划产生)纳入考虑,对于短规划时域而言一直具有挑战性。本文提出了一种新颖的路点模型预测控制(wMPC)概念,用于在线重规划任务。其核心思想是,当路点在当前规划时域内变得可达时,在路点处将规划时域分割,并缩短朝向路点和目标点的时域长度。该方法保持了较低的计算负载,并提供了实时适应变化条件的灵活性。在多路点场景中,所提出的方法在路径长度和轨迹持续时间方面与(全局)离线RRT型规划器相比具有竞争力。此外,wMPC在线动态重规划任务的能力已在KUKA LBR iiwa 14 R820机器人上的动态拾放场景中通过实验得到验证。

英文摘要:

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This paper presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main idea is to split the planning horizon at the waypoint when it becomes reachable within the current planning horizon and reduce the horizon length towards the waypoints and goal points. This approach keeps the computational load low and provides flexibility in adapting to changing conditions in real time. The presented approach achieves competitive path lengths and trajectory durations compared to (global) offline RRT-type planners in a multi-waypoint scenario. Moreover, the ability of wMPC to dynamically replan tasks online is experimentally demonstrated on a KUKA LBR iiwa 14 R820 robot in a dynamic pick-and-place scenario.

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。

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