Research On Following And Obstacle Avoidance Technology Of Intelligent Orchard Mountain Van

With the expansion of orchard planting area in our country,fruit farmers need more intelligent orchard machinery,especially in mountainous orchards with complex geographical environment.At present,most of the operations in mountain orchards in China are in the manual stage,which is not only inefficient,but also prone to safety problems.Therefore,combining with the actual environment of mountain orchard,this paper makes use of the existing chassis of mountain crawler in this laboratory to study the following and obstacle avoidance techniques.A mountain smart van designed to follow fruit growers into orchards for travel between rows and post-harvest transport has been developed.Major research efforts include:In view of the following target positioning problem in the following problem,UWB technology is selected as the positioning method by comparing common wireless positioning methods.The definition of UWB technology and its common ranging methods and positioning methods are introduced in detail.The three-side positioning method is selected as the positioning algorithm of this paper,and a solution is proposed for the ranging error in the three-side positioning algorithm.Finally,a three-side positioning algorithm based on the extended Kalman filter algorithm is studied.The results of MATLAB simulation show that the positioning accuracy of this algorithm is higher than that of single three-edge positioning algorithm and three-edge positioning algorithm based on the least square method.Take differential control mobile car as the research object,establish the kinematic model of mountain intelligent carrier,and carry out kinematic analysis on it.Then the tracking control system is designed according to its kinematics model,and the positioning and tracking experiments are carried out in the laboratory environment.The experimental results show that the positioning error of the UWB positioning system in laboratory environment is less than ±15 cm,and the following error is less than ±20 cm,which meets the experimental needs.Research on local path planning algorithm for autonomous obstacle avoidance problem.By comparing the advantages and disadvantages of common local path planning algorithms artificial potential field algorithm is selected as the local path planning algorithm.Aiming at the problem of unreachable target and local minimum value existing in traditional artificial potential field algorithm,the solution strategy is proposed.The repulsive force field function was added into the influence factor related to the distance between the intelligent truck and the target point to solve the target unreachable problem.The simulated annealing method is integrated into artificial potential field method.To solve the local minimum problem.The results show that the improved artificial potential field algorithm can solve these two problems successfully.On the basis of the mountain track chassis independently developed by the laboratory,a mountain intelligent truck prototype was built and verified in the orchard environment.The results show that the mountain intelligent truck designed in this paper can follow and avoid obstacles well.