Path Optimization of Gluing Robot Based on Improved Genetic Algorithm

The worn belt surface has acceleration characteristics. Thus, timely and rapid maintenance of the microdamage is vital. To quickly and automatically repair the microdamage is a prime target for the gluing robot (GR).To that end, a new variant Traveling Salesman Problem (TSP) is put forward: according to the worn information and GR’s workspace, the worn segments are divided; at a certain worn segment, path optimization of the gluing robot (POGR) problem is presented; then the POGR is simplified into a "double vertices" TSP by Hamilton graph, and the mathematical modeling is built. An improved genetic algorithm (IGA) is proposed to handle the POGR problem, which is called IGA-POGR. The main benefit of the proposed IGA-POGR is the ability to solve POGR of different scales in different ways. The performance of the IGA-POGR is illustrated on four well-known TSP problems and Python simulation is carried out. Numerical results show that IGA-POGR does not give any deviation (0%) from the optimal solution. Compared with discrete particle swarm optimization (DPSO), IGA-POGR has better performance in terms of the solving quality and time consumption when solving four POGR problems.