Research On Autonomous Safe Operation System Of Intelligent Crane

As a modern material handling equipment,bridge cranes are widely used in mechanical equipment manufacturing,port terminals,nuclear power construction and other fields,playing a pivotal role in production.Under the background of “Made in China 2025” and “Industry4.0”,cranes are developing towards unmanned and intelligent direction.In addition,the operation of traditional bridge cranes still relies on manual operation of on-site workers,which not only has low handling efficiency but also is prone to safety accidents.The unmanned and intelligent upgrading of cranes is of great significance for improving handling efficiency,reducing labor costs,and reducing safety accidents.Therefore,in-depth research was conducted around the key technologies of intelligent cranes from three aspects: crane positioning and environmental perception,decision-making planning and trajectory tracking control,to provide theoretical basis and technical support for achieving unmanned driving of cranes.The main work of this paper is as follows:(1)Research on crane positioning and environmental perception technology.Based on UWB positioning technology,crane self-positioning and obstacle perception are realized to make up for the problem that the encoder measurement position is inaccurate when the crane slips and realize complementary with the encoder.The UWB positioning system realizes distance measurement between base station and tag based on TWR(two-way ranging method),and target positioning is realized by using Kalman filtering.The UWB positioning test results show that the UWB positioning system can accurately locate the crane and obstacles with a positioning accuracy of up to 12 cm.(2)Research on crane decision-making planning method.In order to solve the defects of unreachable goals and local optimal solutions in traditional artificial potential field method,an improved algorithm is proposed.The algorithm realizes path planning and obstacle avoidance of cranes in global static environment by improving obstacle repulsion function,increasing repulsion force,setting auxiliary target points,etc.For the obstacle avoidance problem of dynamic obstacles in global environment,a speed planning method based on displacement time(ST)graph is proposed.Based on constructing dynamic obstacle ST graph,this method uses dynamic programming algorithm and quadratic programming algorithm to plan ST curve to achieve dynamic obstacle avoidance and speed planning in global environment.The simulation results of path planning and speed planning show that the crane can avoid static and dynamic obstacles in time by running along the planned path with planned speed,accurately reach the target point safely;The improved artificial potential field model proposed can generate trackable smooth paths.(3)Research on crane trajectory tracking control method.Firstly,the planned path and speed are decomposed according to the two running directions of crane’s main car and small car respectively.Then based on PI feedback control and VF(constant voltage frequency ratio)speed regulation method,AC asynchronous motor of crane’s main car and small car are controlled for speed positioning control to realize trajectory tracking control of crane’s main car and small car.Simulation results show that the designed control system can realize tracking of planned trajectory,effectively avoid obstacles;The distribution of trajectory tracking error measured in experiments is consistent with simulation analysis results;The tracking error of small car’s trajectory is within 8cm,and the positioning accuracy at path end point can reach3mm;The tracking error of main car’s trajectory is within 15 cm,and the positioning accuracy at path end point can reach 4mm;The feasibility and effectiveness of trajectory tracking control scheme are verified.In summary,this paper has conducted research on key technologies of intelligent cranes,proposed an autonomous operation control system architecture for intelligent cranes,realized crane positioning perception during obstacle avoidance handling,decision-making planning and trajectory tracking control.The proposed methods are suitable for practical engineering applications of cranes.