Research On Accurte Grasping Method Based On Dexterous Hand

The dexterous hand is a robot end effector with multiple degrees of freedom,multiple joints,rich sensors,and functions similar to human hands,which can flexibly grasp objects of various shapes.The grasping problem of multi-finger dexterous hands has always been a research hotspot.It can effectively complete the grasping and operation tasks of a variety of objects,and cleverly grasp objects of different shapes and sizes like human hands.The goal of multi-finger dexterous hand research is to enable it to eventually replace human hands and complete delicate and complex operations.The grasping problem of the dexterous hand mainly includes two parts,one is to use the visual sensor to perceive the position,size,shape and other information of the object;the second is to plan the movement of the robot arm and the robot hand on this basis to complete the grasping gesture control,To achieve the purpose of crawling.Therefore,this paper analyzes and studies the robot gripping problem based on the deep reinforcement learning method for the grasping motion planning problem of dexterous hands.First,in order to be able to complete the robot gripping task on the V-REP robot simulation platform,the conversion relationship between various coordinates was studied,a virtual environment for robot gripping was built based on V-REP,and the motion planning of the robot arm was studied Two commonly used algorithms.In addition,for the case of fewer data sets,to avoid the problem of overfitting,this article implements image classification by pre-training Dense Net-121 on a large data set Image Net,which not only avoids overfitting to achieve better Effect,but also shorten the time and save computing resources.At the same time,it is difficult to grasp due to factors such as the smooth surface of balls,cones and other objects,which are easy to slip away when the force is uneven or a little force is applied.Therefore,based on the idea of the three-point fixation method,this article uses the three-finger dexterous hand as the end effector of therobot,and the fingertip of the three-finger dexterous hand as the contact point grasping method,which solves the problem that the object is easy to slide and cause the grab The exact question.In addition,the three-finger dexterous hand is easy to control and has a simple structure,which can realize stable grasping of objects of various shapes.Secondly,in order to be able to select the best crawling action based on the best strategy,a deep reinforcement learning algorithm is used,a crawling network model is established,and the crawling task looks like the task of Markov decision process Training was carried out in the network model,and finally a grab simulation experiment was carried out in the V-REP robot simulation platform,and the grab success rate was taken as the evaluation index of grab performance.Finally,in view of the problem that the objects in the dense scene are not easy to grasp,such as the dense mutual interference of objects,a grasping method of pushing and grasping is proposed.Conducive to accurately grasp the object,improve the success rate of grasping.Simultaneously,in the robot simulation platform,the simulation grab experiment was carried out on objects in dense scenes,which verified the effectiveness of the method.