Study on the Fragrant Pear-Picking Sequences Based on the Multiple Weighting Method

Abstract

The production of the Korla fragrant pear is significant, but the optimal harvesting time is short; therefore, the reasonable use of mechanical arms for harvesting is conducive to promoting the sustainable development of the fragrant pear industry. The efficiency of a robot arm when picking fragrant pears is not only determined by the successful extraction of fragrant pears in a complex environment, but the picking sequence of fragrant pears also directly affects the efficiency of the robot arm. In order to simulate an orchard-picking scenario, this paper built three fragrant pear tree models indoors. The number of fragrant pears on the fragrant pear trees was 5, 10, and 20. Three sets of experiments were designed for comparison with real-world conditions. The main steps were as follows: calibrate the three-dimensional coordinates of each fragrant pear on the fragrant pear trees; determine the end position of the robotic arm at each picking point; find the inverse solution for each group; transform the solutions into matrix form using the rated power of each joint as the weight, and identify the minimum value, which is the angle of each joint in the robotic arm when picking the fragrant pear; use the intelligent socket to find the average energy consumption and average time consumed for picking each group of fragrant pears; and determine the loss ratio of the robotic arm based on the amount of rotation in each joint during picking. The experimental results show that the multiple weighting method reduced the energy consumption by 10.627%, 16.072%, and 24.417%, and the time consumption by 11.988%, 14.428%, and 22.561%, respectively, relative to the hybrid ant colony–particle swarm optimization algorithm, which proves the rationality of the fragrant pear picking order delineated using the multiple weighting method.