Picking robot path planning based on improved ant colony algorithm

With the increasing progress of agricultural production systems, the requirements for the degree of automation are also growing. As the most arduous picking link in the whole agricultural production, the development of picking robots has been paid more and more attention by experts and scholars. How to make the picking robot adapt to the complex and diverse agricultural production environment and effectively replace the manual operation, it is necessary to plan the path of the picking robot carefully. In this paper, a path planning method based on ant colony algorithm is proposed. The basic ant colony algorithm has the shortcomings of slow convergence speed and easy to fall into local optimum so that the final algorithm cannot meet the needs of the target. Firstly, the grid method is used to simulate the agricultural production environment to improve the applicability of path planning. Secondly, a new pheromone initialization scheme is proposed to improve the convergence speed because of the absence of pheromone in the initial time of the basic ant colony algorithm. Then, aiming at the problem that the basic ant colony algorithm is easy to fall into an optimal local solution, a new pheromone initialization scheme is proposed. The pheromone is added or subtracted to make the ant colony converge to the optimal path better. Finally, the performance of the algorithm is improved by choosing the appropriate heuristic function. Simulation experiments show that the path planning based on the improved ant colony algorithm has a good effect on the path planning process of the picking robot.