Research On The Key Problems In Kinematics And Dynamics Of 6R Industrial Robot

As the important supporting equipment of advanced manufacturing industry,industrial robots are the basis for realizing intelligent manufacturing and an important guarantee for realizing industrial automation,digitalization and intelligence in the future.At present,the development of high-precision force-position control system for domestic industrial robots is constrained by the lack of core control technologies such as motion and force.It is urgent to study related problems,innovate methods and realize control algorithms.Therefore,this paper takes the widely used 6R industrial robot(taking ABB IRB 4600 robot as an example)as the research object,discusses the key and difficult problems in the field of kinematics and dynamics,and explores new solutions combined with intelligent optimization algorithm.The main research contents of this paper are as follows:(1)According to the standard D-H parameter method,the kinematics model of6 R industrial robot is established,and the establishment process of the forward kinematics equation of the robot is analyzed.The improved Monte · Carlor method is used to simulate the workspace of the robot,and the effectiveness of the method is verified.With the help of professional mathematical software Maple,the analytical solution formula of inverse kinematics of IRB 4600 robot is deduced,and the general calculation process is formed.The inverse kinematics problem is transformed into the problem of solving nonlinear equations.The optimization objective function is constructed by minimizing the pose error and combining the motion stability principle.The fitness function of the algorithm is designed by linear weighted sum method.(2)Newton Euler method is used to sort out the recursive relationship between the force and torque of each joint of the robot in the standard D-H coordinate system,so as to establish the dynamic model of the robot and realize the programming verification;The problem of dynamic parameter identification of robot is analyzed.The set of dynamic parameters to be identified for the robot without friction and considering friction is obtained respectively;The common methods of parameter identification are introduced.The identification problem is transformed into the optimization problem of nonlinear system.The optimization objective function is constructed to minimize the torque error,and the fitness function is designed accordingly.(3)In order to solve the problems of multiple solutions,low accuracy and poor generality in inverse kinematics,a combinatorial optimization algorithm suitable for all kinds of 6R industrial robots is proposed.An improved whale optimization algorithm(mswoa)is proposed to solve the inverse kinematics problem by using four strategies: chaotic map initialization,nonlinear update of convergence factor,adaptive inertia weight and simulated annealing;The combination algorithm takes the result of mswoa algorithm as the initial value,and then uses Newton Raphson numerical method to quickly iterate the inverse kinematics solution to meet the accuracy requirements.(4)The simulation experiments for solving key kinematics and dynamics problems of multiple groups of robots are carried out in MATLAB platform.The results of inverse kinematics solution show that the performance of the improved whale algorithm is greatly improved;Compared with the direct inversion using whale algorithm,the combinatorial optimization algorithm has the advantages of fast solution speed,good stability and high accuracy.At the same time,it can solve the singular points and the inverse problem of general 6R robot,which proves the feasibility and effectiveness of the algorithm.The PSO algorithm and WOA algorithm are used to identify the dynamic parameters.Through one-time overall identification and step-by-step multiple identification,the model is verified.The results show that the PSO algorithm has better solution performance and step-by-step identification can get more accurate dynamic parameters,which proves the effectiveness of using the optimization algorithm to solve the problem of parameter identification.