Abstract:Aiming at the problems of slow harvesting, an improved rapidly-exploring random trees with visual servoing (VS-IRRT) algorithm was proposed to solve the problems of slow path planning, high path cost and picking failure caused by visual positioning error and joint position error of manipulator in harvesting process. By using the sampling method based on super ellipsoid gravity bias and density reduction strategy, the purpose of tree expansion was increased, the sampling density of tree was reduced and the efficiency of path planning was improved. The greedy idea and B-spline curve were introduced to eliminate unnecessary nodes, and the remaining polyline were smoothed to optimize the implementation effect of the path on the manipulator. Combined with visual servoing control based on translation controller, the influence of positioning error on harvesting process was reduced. Matlab was used to simulate the improved RRT algorithm and the visual servo based on translation controller in two-dimensional and three-dimensional space. The results showed that the number of sampling points of the improved RRT algorithm was reduced by 92.9% compared with that of RRT*-connect algorithm, the planning time was reduced by 86.1% compared with that of RRT*-connect algorithm, and the path cost was reduced by 35.2% compared with that of RRT algorithm. Using six degrees of freedom manipulator for harvesting test, the harvesting speed of VS-IRRT algorithm was increased by 48.36% compared with that of RRT*-connect algorithm, the path cost was reduced by 17.14% compared with that of RRT algorithm, and the harvesting success rate was increased by 2.1 percentage points, therefore, in specific application scenarios, especially in agricultural harvesting scenarios, VS-IRRT algorithm can better improve the comprehensive performance of manipulator harvesting.
