Due to the increasing number of automated guided vehicles (AGVs) in the multi-AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi-AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on.NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi-AGV system.
A dynamic model of swing system of bridge-type ship unloader is established by considering the elastic factor of wire rope in this paper. Based on this model, an improved Negative Zero Vibration (NZV) shaper with optimal control parameters of variable rope length system is proposed to restrain the swing of grab. Then the control effects, influence of elasticity factor and parameter sensitivities are analyzed based on numerical simulation. The results show that the proposed control strategy can reduce the working cycle time of the grab ship unloader about 11% when considering the elastic factor of the wire rope, and the grab's maximum residual swing angle decreases by 67% when discharging at full load, and decreases by 79% when taking the cargos at empty load. This implies that the improved NZV control method provides better swing angle control performance and shorter operation time compared with the Zero Vibration (ZV) and Zero Vibration and Derivative (ZVD) methods. Moreover, elastic rope model can improve the swing angle control effect of grab based on the proposed control strategy compared with rigid rope model. The parameter sensitivity analysis displays that the grab's maximum residual swing angle by using the improved NZV method is sensitive to the change of the grab's center of gravity, and this angle is more sensitive to the wire rope diameter deviation compared with the elastic modulus deviation.
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