Path Planning of Seeding Robot Based on Improved Ant Colony Algorithm
【摘要】：Aiming at solving the problems that using ant colony algorithm in the route planning results in slow convergence speed and too long search time of seeding robot, an improved ant colony algorithm based on grid model is proposed.In order to make the seeding robot travel the shortest path when it arrives at the work place from the starting point, the proposed algorithm is used. In this algorithm,the transfer operator and adjustment operator of butterfly optimization algorithm are introduced into ant colony algorithm to optimize the distribution of original pheromone, solve the blindness of original search path, expand the search space of solution and improve the global of solution; Cauchy mutation operator is used to optimize the probability transfer rule to increase various solutions and improve the convergence speed. In order to verify the superiority of the improved algorithm in the 20 m × 20 m grid map, the simulation experiments of the two algorithms are implemented. Through simulation, it is shown that convergence speed of the improved ant colony algorithm is greatly improved compared with the basic ant colony algorithm. It provides a certain research value for the future research of seeding robot route planning.