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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
