指先近接センサによる把持内操作中の物体姿勢推定
Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation
指先に埋め込んだ光学式近接センサとパーティクルフィルタを用いて、把持内操作中の物体姿勢を推定し、触覚センサより高精度でモデル予測制御の位置決め誤差も低減できることを示した。
著者: Patrick Lancaster, Pratik Gyawali, Christoforos Mavrogiannis, Siddhartha S. Srinivasa, Joshua R. Smith
分類: cs.RO
原文アブストラクト
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.
