Motion Planning And Control Of Manipulator Based On Hybrid Whale Particle Swarm Optimization Algorithm

Under the background of intelligent manufacturing vigorously advocated by the country,industrial robot is used by more and more production enterprises to complete various production tasks in complex environment as an important carrier.The development plan of robot industry defines the main development direction of China's robot industry and puts forward that we should focus on strengthening the research of basic theory and common technology of robot.However,robots made in china generally have the problems of low running speed,unstable operation and low positioning accuracy,resulting in low work efficiency.In order to solve these problems and improve the working efficiency and accuracy of the robot,it is necessary to study motion planning strategy and control method of the robot.In recent years,applying various intelligent optimization methods to the field of robot has been a novel research hotspot.This paper takes the self-developed 6-DOF serial manipulator SNR3-C30 as the research object,uses a hybrid whale particle swarm optimization algorithm as the main optimization tool to study the kinematics,motion planning and trajectory tracking control of the manipulator to improve the accuracy and stability of the manipulator.The main research work done of this paper can be summarized as follows:1.A novel hybrid whale particle swarm optimization algorithm is proposed.Whale optimization algorithm has been used to solve many optimization problems in engineering fields.It has the advantages of simple structure and strong search ability,but it is easy to fall into local optimization.Particle swarm optimization is a widely used optimization algorithm,which has fast local convergence speed,but ability of global optimization is poor.Based on the whale optimization algorithm and particle swarm optimization algorithm,the whale optimization algorithm is improved by employing adaptive threshold and weight.Then IWOA technique and PSO method are combined by crossover strategy and the hybrid whale particle swarm algorithm is proposed.The fusion algorithm flow is designed.The simulation and performance test of 23 test functions are carried out for the proposed algorithm and compared with other six algorithms.The simulation results indicate that the proposed IWOA-PSO algorithm not only has strong local development ability,but also has excellent global exploration ability.It is superior to IWOA and PSO in iteration speed,iteration accuracy and stability.2.Firstly,the kinematics model of the 6-DOF manipulator is established based on the DH parameter method and the kinematics analysis is carried out.The problem of the inverse kinematics of the manipulator is mainly studied.This paper proposes the IWOA-PSO optimized BP neural network algorithm to solve inverse kinematics.It overcomes the shortcomings of low convergence accuracy,slow convergence speed,and easy to fall into local minima when using BP neural network to solve inverse kinematics.The experimental results show that this proposed method improves the accuracy of solving the inverse kinematics of the manipulator and enhances the generalization ability of the neural network.This method is used in the simulation analysis of path planning,trajectory planning and trajectory control.3.From the perspective of manipulator safety,the obstacle avoidance path planning method of manipulator is studied.An improved RRT* based on target offset strategy and adaptive variable step size is proposed this paper.Firstly,the envelope method is used to simplify the model of manipulator and obstacles,and the conditions to avoid collision are analyzed.Then,the obstacle avoidance path planning of manipulator in simple environment and general environment is carried out respectively based on the improved RRT* algorithm,and the B-spline function is used to fit and optimize the path planned by the improved RRT*.Through the comparative analysis of simulation experiments,the results show that the improved RRT* algorithm has better path planning quality than RRT*.4.Aiming at the problem of considering motion efficiency,reducing vibration and improving life of manipulator in the process of motion,the optimal trajectory planning of manipulator is studied.Firstly,based on the collision free path of the manipulator,the quantic B-spline curve was used to interpolate the path to obtain the smooth trajectory of the manipulator.Then,according to the requirements of high efficiency and stability of manipulator,the optimal trajectory planning of manipulator is studied.Taking the running time and jerk of each joint as the optimization objectives,the mathematical model of time-jerk optimal trajectory planning is established by using the weighted coefficient method.Finally,IOWAPSO algorithm is used for time-jerk optimal trajectory plannng.The obtained results demonstrate that the proposed IWOA-PSO can effectively reduce the jerk of the manipulator while improving its work efficiency.5.The trajectory tracking strategies are studied to make the manipulator move according to the planned trajectory with high accuracy in this paper.A PID control method based on IWOA-PSO algorithm is proposed in this work and the simulation analysis of angle tracking control is carried out.In order to overcome the influence of various disturbances and parameter uncertainties in the working process of the manipulator,a fuzzy sliding mode trajectory tracking controller based on IWOA-PSO algorithm is designed by combining sliding mode control and fuzzy control.Then the proposed control method is implemented on the SNR3-C30 manipulator simulation test platform to verify the effectiveness of the proposed control method.