A path optimization algorithm for cooperative operation of multiple unmanned mowers in apple orchard

Objective To improve the work efficiency of cooperative operation of multiple unmanned lawn mowers in modern apple orchard.

Method An improved genetic algorithm (IGA) was proposed to assign and optimize the operating path for each mower. According to the actual operation of unmanned mowers, taking the total turning time and operation time as the comprehensive optimization goal, the optimization model for the operating path of multi-unmanned mower was constructed. In order to solve the model, the genetic algorithm (GA) was improved by setting task thresholds, introducing the improved circle algorithm and Metropolis criterion.

Result Simulation experiments results showed that the IGA balanced the workload assigned to each unmanned mower. Compared with GA, the path optimized by IGA resulted in an average reduction of 22.89% and 19.36% in the total turning time and operation time, respectively, in the rectangular orchard. Compared with the partition operation, the total turning time and operation time of the path optimized by IGA were reduced by an average of 45.53% and 10.68%, respectively, in the rectangular orchard. In the trapezoidal orchard, IGA was not affected by the distribution of fruit trees. Compared with GA and partition operation, the average value of total turning time reduced by 14.38% and 34.08%, respectively, while the average value of operation time reduced by 23.71% and 10.07%, respectively.

Conclusion The proposed IGA performs better and can effectively optimize the operating paths of the fleet, reducing the operation duration and improving work capacity.