Cloud-Side Collaboration-Based Task Allocation Strategy for Agricultural Machine Fleet

Abstract

In order to rationally plan the amount of tasks and task areas for each agricultural robot in the farm, a cloud-side collaborative task allocation scheme is proposed. The cloud platform divides farm tasks based on field obstacles and extracts the center of gravity prime points for each farm task; plasmas as regional task target points through dynamic genetic algorithms for near-field aggregation, after accelerating the solution process by dynamic crossover and variational operators, the Metropolis criterion is introduced to eliminate the local optimal solution of the algorithm and obtain the globally optimal allocation solution. Simulation experiments show that the optimal allocation reduces 9.21%, 5.66%, and 7.21% in the total cost compared to the random allocation, and the feasibility of the algorithm is proved experimentally. Reasonable task allocation can improve the overall production efficiency of agriculture, which is informative for unmanned farms operating in large areas.