| The creation of standardized orchards is an inevitable choice for the development of modern agriculture,hedge wall is an important part of the orchard,with the role of isolation protection and division of the orchard region.With the large-scale establishment of standardized orchards,the task of trimming tall hedge walls has increased.In response to the problems of low efficiency of handheld hedge trimming machinery,high noise,and environmental pollution,applying industrial robots to agricultural equipment,improving hedge pruning devices and raising hedge pruning efficiency has become a research hotspot nowadays.This thesis studies the hedge pruning manipulator from the aspects of path planning,trajectory planning and trajectory tracking control,which has certain practical significance to improve the automation of hedge pruning manipulator.The specific research contents of this paper are as follows:(1)Kinematic and dynamic analysis.The kinematic model of the 4-DOF hedge trimming manipulator was established by using the standard D-H method,the kinematics equation was deduced.The Mento Carlo numerical method was used to obtain the working space of the manipulator and gain the trimming range of the manipulator.The Lagrangian method was used to model the dynamics of the hedge trimming manipulator.(2)Manipulator path planning.Aiming at the random sampling and slow search efficiency of RRT* algorithm,the idea of target deflection probability and gravity were introduced into the sampling process of RRT* algorithm,improved RRT*algorithm,verified the effectiveness and passability of improved algorithm under complex environment and narrow space environment.The hedge obstacle model was established,and the simulation was used to obtain the collision-free path of the manipulator end-effector from the starting point to the end point and the running attitude of the manipulator.The path planning algorithm was run several times,and by comparing the mean and standard deviation of algorithm planning time,path length,number of path points and tree nodes,it can be seen that in case 1,the improved algorithm shortened the path by 66.32% and improved the search efficiency by44.19% compared with the b RRT algorithm;compared with the RRT* algorithm,the path shortened by 10.28% and improved the search efficiency by 40.43%.In case 2,the improved algorithm shortens the path by 67.13% and improves the search efficiency by 45.08% compared with the b RRT algorithm,and shortens the path by2.56% and improves the search efficiency by 73.87% compared with the RRT*algorithm,which proved that the improved algorithm can search for a collision-free shorter path between two points more quickly.(3)Manipulator trajectory planning.On the basis of the obtained path points,cubic B-spline curve interpolation method was used to plan the trajectory of the manipulator.In order to improve the efficiency of the trimming manipulator to a greater extent,the particle swarm algorithm with better optimization performance should be selected to optimize the manipulator running time.With the shortest time as the optimization goal,the particle swarm algorithm with different weights was used to optimize the running time of each segment trajectory,and the time obtained from the solution of Linear differential decreasing weight particle swarm optimization algorithm was chosen as the running time of each segment trajectory by considering the success rate of the algorithm and the curvature of the angular displacement curve,to complete the planning of each joint trajectory,the manipulator running time was reduced by 36.70%..The trajectory in the joint space was calculated by positive kinematics to obtain the trajectory of the actuator end in the Cartesian space,it was verified that the manipulator end-effector and each link can operate collision-free along the planned trajectory.(4)Trajectory tracking control of manipulator.The dynamical system of the manipulator was compensated by fuzzy approximation of the uncertainty term in the dynamics model of the manipulator.To improve the robustness of the system,a robust adaptive fuzzy compensation control law was designed by adding a robust term to the control law,the stability of the designed control law was verified by using Lyapunov function.The trajectory tracking control system was built in MATLAB/Simulink platform,and the simulation comparison analysis of the three control methods shows that the adaptive fuzzy compensation control method reduced the individual joint time absolute error integral(ITAE)values by 54%,89.88%,91.01%,and 94.68%,respectively,compared with the adaptive fuzzy control method;the robust adaptive fuzzy compensation reduced the individual ITAE values by 83.33%,77.98%,72.25%,and 82.95%,which proved that the robust adaptive fuzzy compensation controller designed in this paper has higher tracking accuracy and more stable. |
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