0000000000667889
AUTHOR
Francesco Matraxia
A Neuro-Genetic Approach to Real-Time Visual Grasp Synthesis
Grasping is an essential prerequisite for an agent, either human or robotic, to manipulate various kinds of objects present in the world. It is a fact that we would like robots to have the same skills as we do. However, despite the construction of human-hand-like robotic effectors, much work is still to be done in order to give robots the capability to grasp and manipulate objects. The goal of this work is to automatically perform grasp synthesis of unknown planar objects. In other words, we must compute points on the object's boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. The space of possible config…
Real-Time Visual Grasp Synthesis Using Genetic Algorithms and Neural Networks
This paper addresses the problem of automatic grasp synthesis of unknown planar objects. In other words, we must compute points on the object’s boundary to be reached by the robotic fingers such that the resulting grasp, among infinite possibilities, optimizes some given criteria. Objects to be grasped are represented as superellipses, a family of deformable 2D parametric functions. They can model a large variety of shapes occurring often in practice by changing a small number of parameters. The space of possible grasp configurations is analyzed using genetic algorithms. Several quality criteria from existing literature together with kinematical and mechanical considerations are considered.…