Abstract:In order to solve the problem of agricultural spraying robot path planning, a pathplanning method based on improved ant colony algorithm was put forward. After acquiring actual working environment information and processing working environment with abstract method, grid method was adopted to establish the working environment model of spraying robot. To make the algorithm search more purposeful, heuristic function of ant colony algorithm was redesigned through introducing the target guiding mechanism. Then, state transition probability was ameliorated with new heuristic function. To avoid the algorithm search process stalled and enhance the efficiency of path searching, the pheromone update method was optimized and ameliorated with the strategy of restricting pheromone thresholds and the combination strategies of local updating and global updating of the pheromones. Finally, the simulation experiments were utilized to verify actual results of two algorithms solving the problems of spraying robot path planning. Experimental results show that, the problem of spraying robot path planning can be effectively solved by tradition ant colony algorithm (TACO) and improved ant colony algorithm (IACO). Besides, compared with TACO,IACO not only can effectively shorten program time, but also can improve its convergence performance and enhance its global optimization ability.