Optimization and Experimental Study of an Intelligent Bamboo-Splitting Machine Charging Manipulator

Abstract

A nonautomatic bamboo-splitting machine must charge with material and change tools manually. However, manual charging is very dangerous. An intelligent bamboo-splitting machine can feed automatically and change tools intelligently and has broad application prospects. A charging manipulator is an important part of an intelligent bamboo-splitting machine. The size of the manipulator was optimized here using a genetic algorithm. The capture rate, centering rate, and dynamic characteristics of an intelligent bamboo-splitting machine charging manipulator, in which key factors were considered, were experimentally studied. First, three different manipulators, with arm lengths at 210, 220, and 230 mm, were developed. Then, the bamboo materials were divided into three gradients (60–85, 85–110, and 110–135 mm) according to diameter ranges. Accelerators were used to measure the manipulator arm dynamic characteristics, and a high-speed charge-coupled device was used to record the grasping process. Experimental results showed that the manipulator capture rate with an arm length of = 220 mm was as high as 100%, but that of manipulators with arm lengths of = 210 and 230 mm was 96 and 98.67%, respectively. Thus, the manipulator with a 220 mm arm length showed better performance than the other two manipulators. Trend curves of the influence of material diameter on capture time were similar to an exponential function.