Research On Trajectory Optimization Of 6-DOF Industrial Robots

With the increasing application of industrial robots in various fields,higher requirements for trajectory planning are put forward.Working efficiency and energy consumption have always been the most important performance indicators for industrial robots.This paper takes a 150Kg 6-DOF heavy-duty robot as research object.The kinematic model of industrial robot is established by D-H method,and the Lagrange method is used to derive the dynamic equation.We build the 3D solid model of the robot in SolidWorks and then the relevant kinematics and dynamics parameters are obtained.The problem of time-energy optimal trajectory planning is researched based on intelligent optimization algorithms.The traditional polynomial trajectory planning method does not consider the constraints.Therefore,this paper studies the time-energy optimal trajectory planning method which meets the dynamics constraints based on intelligent optimization algorithm.Aiming at the problems of slow convergence and low accuracy of artificial bee colony algorithm(ABC)?an improved artificial bee colony algorithm(IABC)is proposed.In order to improve the weakness of ABC algorithm only searching one single dimension per iteration,a more efficient full-dimensional search strategy is used in this paper.In the employed bee phase,the guidance term based on the elite solutions and the disturbance term based on the random solutions are introduced to improve the guidance of elite solutions and maintain the diversity of the population.In the onlookers phase,a composite search strategy is used.Some particles perform the full-dimensional neighborhood search to ensure the global exploration ability of the algorithm,while the others execute the search strategy of the Grey Wolf Optimizer to enhance the the local exploitation ability.The benchmark function is used to test the performance of algorithms,the result shows that the performance of IABC is better than that of ABC?GWO?MABC and GABC.Gravity search algorithm is a novel optimization algorithm.However,the search mechanism of the standard algorithm will result in the loss of diversity of the population at the later stage of iteration,which will lead to the low accuracy and premature convergence of the algorithm.Therefore,inspired by neighborhood search strategy and greedy selection mechanism of ABC,an improved gravity search algorithm(IGSA)is obtained.The agents of the gravitational search algorithm are divided into two groups according to the size of the inertial mass.Firstly,the particles of the leading group perform a small-range neighborhood search.Then the leading group guide the onlookers group to update positions by applying gravity.Thus the guiding role of elite solutions is enhanced to promote the convergence process.Aiming at the multi-objective trajectory optimization problem with optimal travelling time and energy consumption,the energy consumption model is established on the basis of considering dynamics.Considering energy saving and efficiency synthetically,the cost function is used as a weighted sum of traveling time and energy consumption.Optimal trajectory planning methods are proposed based on IABC and IGSA algorithms respectively.The simulation is carried out based on the MATLAB,and the motion control program developed independently based on NI Compact RIO embedded processor is used for experiments.Then the effectiveness of the improvement strategies and the feasibility of applying the optimal trajectory planning method to practical work can be verified.