A non-cooperative target grasping position prediction model for tethered space robot

Abstract

How to identify proper grasping positions on non-cooperative targets is an essential technology for tethered space robots when implementing on-orbit service. To localize unknown targets, most template-based methods require a large set of well-engineered templates. Sliding them over the image to decide potential target positions is exhaustive and inefficient. To solve this rather complex problem, we propose a novel object localization method by predicting object regions before extracting them. It can reduce the search area of targets remarkably and runs fast. Firstly, the features of histogram of oriented gradients are modified to be more discriminative. Then the cascaded support vector machine is used to select better proposals over scales and aspect ratios. We also integrate the pre-processing procedure to highlight active target features. Further experiments demonstrate that our method improves the detection rate in VOC 2007 favorably and performs well in satellite brackets localization.