Research On Trajectory Optimization Of Delta Robot Based On Food Packaging

Because of its fast and stable characteristics,industrial robots have widely used in the field of food pickup,sorting,packaging and other work.This thesis take the Delta robot of packaged food as the research object,carries out motion planning.According to energy consumption and time,it carries trajectory optimization,obtains efficient and low energy consumption trajectory scheme,and builds an experimental platform for verification.The main contents are as follows:Firstly,the characteristics of the mechanism are analyzed,and the virtual model of the Delta robot is established.The inverse kinematics is solved based on the length equation of the follower rod.The forward kinematics is solved by the function optimization algorithm combining genetic algorithm and nonlinear programming.The kinematics theory is verified by an example,so as to obtain the theoretical maximum workspace and full workspace,which is convenient for the subsequent planning of the moving platform trajectory of food packaging.On the basis of the mass equivalent distribution of the follower rod,the dynamic equation is constructed based on the principle of virtual work.Secondly,PH curve was used to smooth the gate path in the aspect of path planning.In terms of trajectory planning,due to the limitations of the TDTR algorithm,an improved trajectory compression algorithm is formed by combining the improved discrete differential evolution algorithm,and the trajectory feature points extracted by the algorithm are used as interpolation points for interpolation.Based on the planned trajectory example,the improved trajectory compression algorithm and TDTR algorithm are used to compress the joint trajectory respectively.The data comparison shows that the improved trajectory compression algorithm reduces the jitter of the driving motor and the energy consumption of the robot,and the moving platform moves smoothly,which proves the effectiveness of the algorithm in trajectory planning and simulation.At the same time,based on the trajectory example,the ADAMS software is used to simulate and verify the robot dynamics theory.Then,the improved multi-objective particle swarm optimization algorithm is used to optimize the trajectory by taking the segmented trajectory time of the moving platform as the optimization variable,and the energy consumption and time performance indicators are defined.Based on the Euclidean distance,the compromise planning is carried out,and the time and energy consumption are weighed to obtain the comprehensive optimal trajectory scheme from the Pareto optimal solution set.Compared with the trajectory examples,the energy consumption of the optimized robot is reduced by 5.35 % and the time is reduced by 6.07 %.The algorithm reduces energy consumption and improves work efficiency.The trajectory interpolation data of the driving joint and the simulation data of the moving platform in the comprehensive optimal trajectory scheme and the trajectory instance are studied respectively.The comparison shows that compared with the trajectory instance,the maximum velocity and velocity variation of each branch joint are reduced,the interference and impact of the robot are small,and the motion is stable.Finally,the experimental platform of Delta robot is built to verify the comprehensive optimal trajectory scheme in the aspect of experiment.By comparing the experimental data and simulation data of the rotation angle of each branch drive joint,it can be seen that the error is within a reasonable range.The drive joint of each branch moves smoothly according to the preset trajectory,which verifies the effectiveness of the algorithm and the feasibility of the comprehensive optimal trajectory scheme.