Study on the whole area coverage of agricultural robot in complex environment based on ant colony-BFS algorithm

Objective  To solve the whole area coverage problem of agricultural robot in digital ecological farm with path repetition rate as the optimization target.

Method  Firstly, the obstacles in the raster map were expanded and rectangular partitioning was performed and the partitions were merged. Then, the improved ant colony algorithm was used to calculate the traversal order among partitions, and the improved breadth first search (BFS) algorithm was used to calculate the connecting path between the end point and the starting point between partitions. By this way, the whole area coverage of robot could be achieved. The improvement schemes of the two algorithms were as following: The selection and crossover operators of genetic algorithm were improved by artificial immune algorithm and particle swarm optimization algorithm respectively. The improved selection and crossover operators, mutation operator of original genetic algorithm and ant colony algorithm were combined to improve the pheromone updating method of traditional ant colony algorithm. The dynamic function was established to simplify the path planned by BFS algorithm.

Result  The simulation results showed that the number of iterations and path length of the improved ant colony algorithm were 83.1% and 4.8% less than those of the traditional ant colony algorithm. The path repetition rate of the improved ant colony algorithm and the improved BFS algorithm was 56% of the traditional ant colony algorithm and BFS algorithm, and the agricultural robot could achieve 100% coverage of farmland.

Conclusion  This study provides a solution for agricultural robots to perform the whole area coverage in digital ecological circular farm with complex environments.