Research On Path-planning Of Intelligent Wheeled Robot In Farms

In the field of robot research,path planning has always been the focus of the research.In recent years,with the rapid development of robot technology and industry,more and more scholars and experts pay attention to the research of path planning.At present,the research on path planning method has achieved fruitful results,but there are also shortcomings.Many algorithms are proposed based on the simulation,and they can not be used in practice.These problems need to be further improved by the researchers.In this paper,combining with the project of Jilin Provincial Department of Education,the design of the control system of the large scale farm monitoring robot,path planning of the robot is studied.Firstly,aiming at the problem of path planning of the robot in static environment,an improved genetic algorithm is proposed.In this method,by introducing the static obstacles into the initial population directly,one is to improve the establishment of the environment map and avoid the environment modeling problem.Two is to set the inspection devicein the algorithm and to guarantee that the generation of new individuals is not in the obstacle.Three is to improve the design of the fitness function by taking into account the shortest path distance,path smoothness and safety performance(avoid obstacles)of the robot simultaneously.Four is to adaptartificially to the weight coefficient for the three factors ofthe fitness function,and to further ensure the optimal path.The simulation results show that the method is feasible.Secondly,based on the improved genetic algorithm,the robot path planning method is optimized.According to the weights of the fitness function in the genetic algorithm,the particle swarm optimization algorithm is used to optimize the robot.According to the relationship of the three weights in the fitness function of the genetic algorithm,the fitness function of the particle swarm optimization algorithm is designed.The method can determine the weight of each factor,and realize the autonomous coordination of the weighting factor,so as to get the optimal path.The simulation results show that the method is feasible.Thirdly,the ant colony algorithm is used to simulate the path of the robot under static environment.The principle,parameters and basic formula model of ant colony algorithm are analyzed.The feasibility of the algorithm is obtained by simulation experiment.Finally,according to the actual environment of the farm,the simulation of the robot path planning is carried out.Based on the environment of the chicken farm,the monitoring mode(cruise mode)and the path planning model were studied according to the different tasks of the robot in the chicken farm.In different working modes,the path planning method of robot is different.In the monitoring mode,the step size method is adopted,and the genetic algorithm and ant colony algorithm are adopted in the path planning mode.According to the obtained simulationresults of genetic algorithm and the ant colony algorithm,the genetic algorithm is more suitable for the robot path planning in the static environment.Compared with the ant colony algorithm,the genetic algorithm is more advantageous.