Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization

Abstract

The product assembly process in industrial production is a time-consuming and labour-intensive link, so the use of robots for intelligent assembly can achieve high-efficiency operations to a large extent. During the intelligent assembly process, robots need to carry out three key processes: product information modelling, assembly route planning and assembly sequence planning. The experiment examines assembly sequence planning and assembly route planning. In light of the deficiencies of the current assembly sequence planning technique, we propose a new approach, which is difficult to calculate, the experiment proposes to introduce the DPSO algorithm into the assembly sequence planning model. Meanwhile, to prevent the model from entering local optimal, the experiment adds disturbance operation on the basis of DPSO. That is, a part is randomly selected to be inserted into another position in the disassembly sequence and at the same time, the rest of the parts are moved to form a new disassembly sequence to update the particle's location. In addition to the assembly sequence planning, the experiment aims to improve the planning path obtained by the existing RRT algorithm, which is not smooth enough, that is, to simplify the installation path by reducing the change of direction. The simulation experiment findings demonstrate that the addition of the disturbance to DPSO algorithm successfully prevents the model from entering a local optimum state. The moving distance of the parts before path optimization is 52.0012mm and the number of moving direction changes is 8 times, while the moving distance of the parts after path optimization is reduced to 42.8094mm and the moving direction is changed only 2 times.