| The inedible root cutting is one of the most time-consuming and labour-intensive processes in the whole process,and automating the root cutting process is an urgent problem.Due to the brittle growth of chanterelle mushrooms,the picking and processing process requires a high level of robot core-the ‘controller’;the kinematic-based position error control requires a high level of servo drive,and the robot is unable to respond in a timely manner in the event of an accident,which can cause damage to the robot and even threaten the lives of staff.The kinetic model-based torque error control can ensure high-precision trajectory tracking control of the robot joints,while also improving the dynamic characteristics of the robot and increasing the speed of error convergence.This paper investigates the kinetic model-based moment error control of a robot gripping mechanism based on the design of the mushroom picking process,to achieve efficient and smooth gripping of the mushroom by the robot.The main research elements of this paper are.Firstly,a complete process flow for the harvesting and processing of the Chanterelle mushroom is designed.The mechanical system of the entire harvesting process was divided into a picking mechanism,a transfer mechanism,a clamping mechanism and a cutting mechanism.By analysing the application of the main machines currently on the market,it was decided to use a SCARA robot as the robot for the clamping mechanism.Secondly,the kinematic and dynamical models of the clamping mechanism robot are established;the positive and inverse kinematic models of the robot are derived using the D-H parameter transformation of the robot,and the Jacobi matrix of the robot is derived to realise the coordinate transformation of the robot workspace and task space;the robot dynamics model of the clamping mechanism is established by the Lagrangian method,and for the existence of friction in the robot joints,the linear function is used to approximate the Stribeck friction model.The minimum set of parameters of the robot dynamics is found by eliminating some linearly related parameters through linear transformation.After that due to the low accuracy of the parameter identification,the identification algorithm is optimised and the traditional least squares method is replaced by an improved genetic algorithm.Then the basic process of kinetic parameter identification is followed to identify the parameters of the robot of the clamping mechanism.Finally,it is verified through experiments that the accuracy of the improved genetic parameter identification is higher than that of the least squares method,which can replace the least squares method as a parameter identification algorithm and provide a basis for the development of kinetic model-based trajectory tracking control for the gripper robot.The next step is to investigate the trajectory tracking control of a robot with a clamping mechanism.As the identified kinetic parameters cannot be exactly equal to the actual values,the factors affecting the accuracy of the kinetic model are analysed to be mainly caused by the inaccuracy of the friction model during modelling and external disturbances.Therefore,computational torque control is used as the main controller,and then a fuzzy RBF neural network adaptive compensation control strategy is applied for external disturbances and friction inaccuracies.The designed fuzzy RBF neural network is a Mamdani type with a five-layer structure,which dynamically adjusts the center and width of the radial basis function.The fuzzy RBF neural network is used to approximate the unmodeled nonlinear part of the dynamics for feedback control,and a sliding mode surface is introduced to suppress the instability of the system.Simulation experiments show that the use of the fuzzy RBF neural network compensation control strategy reduces joint tracking errors and time lags compared to pure computational torque control.High-precision trajectory tracking control and planar positioning of the robot are achieved,providing a control strategy for efficient and smooth grasping of chicken leg mushrooms by the robot. |
