Comparative Evaluation of Approaches for Determination of Grasp Points on Objects, Manipulated by Robotic Systems

Abstract

This paper considers comparative evaluation of recent methods for grip point determination for manipulations with objects in the scene. This research is aimed to compare and evaluate the modern approaches of grip point determination, when this process is aided by computer vision. The methods of object gripping, considered in this paper, are employed in connection with depth map composition, backed by neural network model ResNet-50, which allowed to omit application of specific depth sensors in the course of experiments. This research shows dependencies of successful grip probability from the things being manipulated. Probability scores, averaged over different types of objects for the methods GPD, 6-DOF GraspNet, VPG, were, accordingly, 0.690, 0.741, 0.613. The paper also considers dependencies of successful grip probability from object sizes, distances from the capturing camera and target objects in the scene, luminosity levels, as well from the angles of scene inspection along the vertical axis. In terms of the considered methods GPD, 6-DOF GraspNet, VPG, non-linear increasing dependencies are revealed for object type-averaged probabilities of successful grip from luminosity level of the scene. It was also discovered, that the dependencies of successful grip for all the other parameters are non-linear and non-monotonic. The ranges of the values for scene parameters under consideration are defined in this paper, which ensure the highest probability values for object grip in these approaches. Upon the results of the performed experimental evaluation, the 6-DOF GraspNet solution showed the best performance for the vast majority of the considered parameters of the scene. The approach, presented in this paper, is the preferable way for solution of grip point problem, in context of methods, which assume depth map reconstruction without specific equipment.