Best Viewpoints for External Robots or Sensors Assisting Other Robots
Best Viewpoints for External Robots or Sensors Assisting Other Robots
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DOI：
10.1109/thms.2021.3090765
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发表时间：
2021-08-01
影响因子：
3.6
通讯作者：
Murphy, Robin R.
中科院分区：
文献类型：
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作者：
Dufek, Jan;Xiao, Xues;Murphy, Robin R.
这项工作创建了一个执行任务的机器人的不同外部视点的价值模型。这种做法的当前状态是使用远程操作的助理机器人来提供由主要机器人执行的任务的视图；然而，视点的选择是临时的，并不总是导致性能的提高。本研究运用心理运动学的方法，利用吉布森的启示，建立了外部观点相对质量的模型。在这种方法中，基于人类操作者的精神运动行为对提供的视点进行评级，并将其聚为具有等价值的视点流形。在与31名专家机器人操作员进行的一项研究中，使用基于计算机的两个机器人的模拟器，对30个视点的值进行了量化，以获得四种负担(可达性、通过性、可操作性和可穿透性)。使用凝聚层次聚类将具有相似值的相邻视点聚类成排序流形。结果表明，存在视点值具有统计显著差异的流形，视点值在统计上显著依赖于提供度，并且视点值独立于机器人，从而验证了基于提供度的方法的有效性。此外，对于每个启示，最好的流形在性能(将时间改进14%-59%，误差减少87%-100%)方面具有较大的Cohen‘s d效应(1.1-2.3)，并且在性能变化方面比最差的流形提供显著的改进。该模型将使辅助机器人能够自主选择可能的最佳视点和路径规划。
This work creates a model of the value of different external viewpoints of a robot performing tasks. The current state of the practice is to use a teleoperated assistant robot to provide a view of a task being performed by a primary robot; however, the choice of viewpoints is ad hoc and does not always lead to improved performance. This research applies a psychomotor approach to develop a model of the relative quality of external viewpoints using Gibsonian affordances. In this approach, viewpoints for the affordances are rated based on the psychomotor behavior of human operators and clustered into manifolds of viewpoints with the equivalent value. The value of 30 viewpoints is quantified in a study with 31 expert robot operators for four affordances (reachability, passability, manipulability, and traversability) using a computer-based simulator of two robots. The adjacent viewpoints with similar values are clustered into ranked manifolds using agglomerative hierarchical clustering. The results show the validity of the affordance-based approach by confirming that there are manifolds of statistically significantly different viewpoint values, viewpoint values are statistically significantly dependent on the affordances, and viewpoint values are independent of a robot. Furthermore, the best manifold for each affordance provides a statistically significant improvement with a large Cohen's d effect size (1.1-2.3) in the performance (improving time by 14%-59% and reducing errors by 87%-100%) and improvement in the performance variation over the worst manifold. This model will enable autonomous selection of the best possible viewpoint and path planning for the assistant robot.