Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm

Robot-based assembly sequence planning plays an important role in product design and has been widely researched in the macro world. But in the micro world, the characteristics of microrobot-based assembly, such as complexity and scaling effects, make the assembly problems much more difficult and seldom researched. In this paper, the microrobot-based micro-assembly sequence planning problem is discussed. The problem is transferred as a combinatorial optimization problem with several matrixes, such as the moving wedge matrix, the microrobot performance matrix, and the sensing matrix. Furthermore, the geometrical and visibility constraints of assembly sequence and evaluation criteria for optimization are given. A particle swarm optimization (PSO) algorithm modified ant colony optimization (ACO) algorithm, called a hybrid PS-ACO, is devised to solve the problem efficiently. The combination of local search and global search of PSO is introduced into the ACO algorithm, which can balance the exploration and exploitation performances of searches. The experimental results have shown that the PS-ACO can solve the micro-assembly sequence planning problem with better convergence performance and optimizing efficiency than basic ACO and GA.