Research On Motion Planning Of Fruit Picking Robot

China is a big country of fruit planting and production.Fruit picking is a very important part of fruit production.It is of great historic significance for accelerating the process of agricultural modernization to realize automatic fruit picking through picking robots.Fruit harvesting is mostly carried out in orchards,which is characterized by many obstacles and complex structures.Therefore,in order to complete the picking task smoothly,it is necessary not only to locate the picking target accurately,but also to plan the trajectory of the picking robot reasonably.In order not to damage fruit trees and fruits,path planning is needed to make the robot avoid obstacles and reach the target point accurately.In order to improve the picking efficiency and maintain the smooth movement of the robot,trajectory planning of the robot arm is needed to make the robot reach the target point quickly and smoothly.The motion planning of the picking robot directly affects the efficiency of fruit picking.Therefore,in order to achieve non-destructive and efficient picking operation,it is necessary to study the path planning and trajectory planning of the picking robot.In this paper,an improved rapidly exploring random tree(RRT)algorithm is proposed for path planning of picking manipulator,which was validated by three-dimensional virtual scene experiments and actual picking experiments.B-spline curve and particle swarm optimization(PSO)algorithm were used to obtain the trajectory of the manipulator.The trajectory optimization of the manipulator under multi-objective and multi-constraint conditions was studied in depth.The optimal Pareto solution set of the trajectory of the manipulator was obtained,which provides a basis for the motion control of the picking robot.The main research contents and work are as follows:(1)The kinematics and dynamics modeling of the picking manipulator were studied.The kinematics model of the picking manipulator was established based on D-H parameter method,and the inverse kinematics solution of the manipulator was obtained by algebraic method and simulation degradation method respectively.The dynamic equation of the picking manipulator was deduced based on Lagrange method,which is the basis of the research on the collision-free path planning and trajectory planning of the manipulator.(2)Aiming to realize the obstacle avoidance of the fruit-picking robot in dynamic and unstructured environments,an improved RRT algorithm is presented in this study.By establishing the collision detection model of the manipulator and obstacles,the obstacle avoidance path planning is carried out.The idea of target gravity was adopted to accelerate the path search speed.To optimize the path generated by RRT,a genetic algorithm(GA)and smooth processing were proposed.Performance tests in a virtual environment showed that the optimal combination of step sizes in the path search were 1.6 degrees for step size 1 and 2degrees for step size 2.The time to achieve the path was 0.47 s,and the success rate was100%.The path length was shortened by 20%after optimization.Experiments in the real environment were carried out for the 6 degrees of freedom(DOF)picking manipulator to pick litchi,and the results showed that the collision-free path planned by the proposed algorithm could successfully drive the manipulator from its initial position to the goal position without any collision.(3)Aiming at the trajectory planning problem of picking manipulator in joint space,B-spline curve and particle swarm optimization algorithm were used to plan and optimize the trajectory.Firstly,based on the manipulator collision-free path,the B-spline curve was used to interpolate the path to obtain the smooth trajectory of the manipulator,which satisfies the kinematic and dynamic constraints of the manipulator.Then,PSO algorithm was used to optimize the trajectory of the picking operation in order to meet the requirements of high efficiency and stability.The simulation results showed that the trajectory motion time optimized by PSO algorithm was 7.87 s,and the trajectory pulsation index optimized by PSO algorithm was 65.08o/s~3.The pulsation of the motion trajectory with the best time performance was larger,which results in the greater impact of the manipulator joints and is easy to cause joint damage.The motion trajectory with the best pulsation performance was very stable,but the movement time was longer,which leads to the lower work efficiency.(4)Aiming at the problem that there is conflict between objectives in single objective optimization of manipulator trajectory,optimization idea and Pareto optimal solution are adopted to simultaneously optimize multiple objectives.Multi-objective particle swarm optimization(MOPSO)was used to optimize the trajectory of the manipulator.Aiming at the problem that the algorithm is easy to fall into the local optimum because of the loss of population diversity,methods of adding mutation operator to the external file set and adding annealing factor in the process of population regeneration were adopted to maintain the diversity of the population and escape from the local optimum.Aiming at the multiple kinematics and dynamics constraints in the trajectory planning of manipulator,the Pareto constraint domination method was used to compare the advantages and disadvantages of particles,and the dominant relations among them were obtained.Aiming at the problem that bad particles in the population affect the convergence speed of the algorithm,the elimination and supplement strategy can not only eliminate bad individuals,but also add the excellent particles in the external archives to the population,which improves the convergence speed of the population.The classical test function was used to verify the improved MOPSO algorithm,and the results were compared with those obtained by MOPSO algorithm and NSGA-II algorithm.The simulation results showed that the Pareto optimal solution set obtained by the improved MOPSO algorithm in this paper has better convergence,distribution and stability.Finally,the improved MOPSO algorithm was used to optimize the multi-objective problem of the manipulator,and the optimal solution set close to the real Pareto front is obtained,which provided a basis for the trajectory planning of the manipulator.(5)For different types of picking manipulators,taking litchi,orange and apple as harvesting objects,the motion planning algorithm proposed in this paper,combined with visual system,was used to carry out picking experiments.Obstacles and target position information obtained by visual system were used as input of motion planning algorithm,and joint motion trajectory was used as output.The motion trajectory was input to the manipulator to guide its motion.The experimental results showed that the picking manipulator picked the target fruit smoothly,and the movement was stable,and the picking efficiency was high,which verified the effectiveness and practicability of the motion planning algorithm in this paper.