Research On Path Planning And Time-optimal Trajectory Planning Of Apple Picking Robot

With the accelerated progression of high technology,the application of robotics is increasing day by day in the fields of industry,agriculture,medical care,logistics,and so on.China is the world’s largest apple planting country,with planting area and annual production exceeding 50%.In the agricultural field,apple picking robots can replace human beings to complete picking operations autonomously,reducing human labor and improving picking efficiency.Path planning and time-optimal trajectory planning techniques are pivotal technologies in robot navigation,which affect navigation and robot control accuracy.In this paper,based on the apple picking robot,MOTOMAN-SSF2000 six-degree-of-freedom industrial-type robot arm is selected as the research object to study the kinematic analysis,path planning,and time-optimal trajectory planning of the picking robot.The main elements include:(1)According to the characteristics of the complexity of the apple growing environment,the design principles of the picking robot are summarized,the threedimensional model of the apple is reconstructed by extracting feature points with five polynomial fits,and the target identification and positioning of the robot are analyzed,and the overall scheme of the apple picking robot consisting of crawler mobile platform,end-effector,and six-degree-of-freedom robot arm is designed.(2)The standard D-H parameter method is chosen to create the kinematic theoretical model of the apple picking robot,analyze and solve its positive and negative kinematics,and verify the results with the help of Matlab Robotics Toolbox.Relying on the Monte Carlo method to solve the workspace of the picking robot,the advantages,and disadvantages of the Cartesian and joint coordinate systems are analyzed,and the joint space is chosen as the search space of the picking robot arm.(3)The collision detection problem in path planning is analyzed,and the collision detection model is reduced to the problem of determining the position relationship of geometric bodies in space.The cylindrical envelope picking robotic arm linkage is used,the envelope ball is selected to envelop the environmental obstacles,and the dimensions of the linkage are converted to the obstacle envelope ball using the idea of transformation superposition.(4)Due to the drawbacks of randomness and single fixed step size of node expansion of RRT and derivative algorithms,an improved RRT* algorithm based on the artificial potential field method is proposed.Based on the artificial potential field method to set the goal orientation,adjust the direction of node expansion,introduce the adaptive step size strategy and node expansion strategy to adjust the step size of node expansion,and improve the path planning efficiency.The pruning function is used for preprocessing and five times B-sample curve fitting to smooth the path.The path planning simulation experiments are conducted by Matlab to compare the RRT*algorithm and RRT algorithm,and the simulation experiment results show that the improved RRT* algorithm proposed in this paper has a certain reduction in path length,running time,the number of sampling nodes and search times in single-obstacle and multi-obstacle picking environments.It can efficiently plan a safe and contact-free collision picking path from the start position to the target position,which verifies the effectiveness of the improved algorithm.(5)Under the kinematic constraints of the picking robot,the time-optimal trajectory planning of the apple picking robot is carried out by using five times Bsample curves to generate continuous smooth trajectory curves with time as the optimization objective.An improved whale optimization algorithm is proposed to optimize the picking working time of the picking robot by introducing an adaptive weight strategy,and simulation experiments are conducted by Matlab.The simulation experimental results show that,compared with the whale optimization algorithm and genetic algorithm,the improved whale optimization algorithm converges faster and obtains the global optimal solution under satisfying the kinematic constraints,and the picking robot motion time is reduced from 16.80 s to 7.828 s,a time reduction of 53.40%,which improves the picking efficiency and effectively proves the superiority of the improved algorithm.