Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation
Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation
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DOI：
10.1109/iros47612.2022.9981692
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发表时间：
2022-04
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影响因子：
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通讯作者：
Patrick E. Lancaster;Pratik Gyawali;Christoforos Mavrogiannis;S. Srinivasa;Joshua R. Smith
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作者：
Patrick E. Lancaster;Pratik Gyawali;Christoforos Mavrogiannis;S. Srinivasa;Joshua R. Smith
在手部操作过程中，机器人必须能够连续地估计对象的姿势，以便产生适当的控制动作。位姿估计算法的性能取决于机器人的传感器是否能够检测出具有区别性的几何对象特征，但以前的传感器无法稳健地进行此类测量。机器人的手指可以遮挡环境或机器人安装的图像传感器的视线，而触觉传感器只能在接触的局部区域进行测量。基于指尖嵌入的接近传感器对遮挡的健壮性和超越局部接触区域的测量能力，我们首次评估了基于接近传感器的手部操作的位姿估计。我们开发了一种新型的具有指尖嵌入光学飞行时间接近传感器的双指手，作为平面手部操作中的位姿估计的试验台。在这里，手部操作任务包括机器人将圆柱形物体从其工作空间的一端移动到另一端。我们证明，在手部操作中，基于粒子滤波的基于接近传感器的位姿估计具有统计学意义：a)与基于触觉传感器的基线相比，平均位姿误差降低50%；b)使模型预测控制器与基于触觉传感器的位姿估计相比，最终定位误差降低30%。
During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.