Reactive phase and task space adaptation for robust motion execution

Reactive phase and task space adaptation for robust motion execution
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反应阶段和任务空间适应稳健的运动执行

DOI:
10.1109/iros.2014.6942548
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发表时间:
2014
期刊:
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子:
--
通讯作者:
M. Toussaint
M. Toussaint
中科院分区:
--
文献类型:
--
作者:
P. Englert;M. Toussaint

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让机器人在现实环境中取得成功的一个重要方面是赋予它们在不断变化的情况下稳健地执行运动的能力。传统的运动规划方法通常为静态环境创建规划。在动态环境中直接执行此类计划往往会出现问题。我们提出了一种通过将环境的变化反馈到任务空间中对运动计划的变换来适应运动计划的方法。此外,计划中的进度被定义为根据实际任务进度自适应更新的阶段变量。该阶段变量释放了许多运动规划方法带来的严格的时间一致性。我们方法的主要好处是能够在运动执行期间以计算高效的方式进行这种适应。因此,通过不断地将参考平面的几何和动态特征转换到当前情况,弥合了运动规划和运动执行阶段之间的差距。我们通过将该方法与其他方法进行比较来评估该方法的性能,如动态运动基元和在几个模拟基准任务上的连续重规划。此外,我们还在PR2机器人平台上演示了真实机器人的适用性。
An essential aspect for making robots succeed in real-world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.
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