Research On Path Planning Of Hoisting System Of Overhead Crane

In recent years,with the continuous improvement of the automation and intelligence level of various industries,the large engineering machinery is developing in this direction.Intelligent path planning as an important research direction in the field of robotics has been more mature and widely used,but in large engineering machinery such as crane research is relatively small.Therefore,based on the characteristics of the bridge crane and the existing research literature in the lack of fusion grid method,ant colony algorithm,and particle swarm algorithm is used in the dynamic environment,proposed a new path planning method of hoisting crane in static and dynamic environment,the main contents are as follows:Firstly,based on the improved ant colony algorithm,this paper proposes a method of lifting path planning.Study on static environment in known obstacles,ignoring the hook lifting crane,the environment was simplified to a two-dimensional grid model of ant colony algorithm,heuristic function,volatile coefficient was improved,and taking into account the operation characteristic of bridge crane,in order to avoid the size of car frequent braking,will slash the path of the original straight to and,a cost function is introduced to evaluate the effect of simulation.The simulation results show the feasibility of the algorithm.Secondly,the 3D model of bridge crane working environment and static known environment,uses the grid method to divide the space,the overhead crane hoisted weight maximum swing distance and safety through the obstacle distance and a safe distance,the ant colony algorithm,heuristic function of evaporation coefficient as well as the fitness function of the improved and lifting path planning applied to the bridge crane 3D environment.The simulation results in MATLAB show that the scheme is feasible and effective.Finally,according to the dynamic hoisting environment,this paper puts forward the bridge crane hoisting path planning applying to the dynamic environment.By grid method through modeling,will be the preferred choice of thought,sensitive particles according to the fitness value change to detect the environment and according to the threshold trigger response mechanism,and the dynamic particle swarm algorithm the fitness value change of planning.In order to avoid the frequent braking of the big car,a straight line is used.The simulation results show that this method can be used not only in the dynamic environment,but also in security.