An Evaluation System for Multi-Machine Cooperative Operation of Weeding Robots Based on Fuzzy Combination Weight

This paper proposes a matter-element evaluation model to address the low-efficiency issue in the multi-machine cooperative operation system of weeding robots. The model transforms the weeding robot's multi-machine cooperative operation system into an indicator system with five first-level indicators: working environment, safety, path planning, energy consumption, and weeding situation. Each first-level indicator has several second-level indicators. Each second-level indicator is weighted with a combination of the order relation analysis method and the CRITIC method. The combination weighting is then used to determine the weight of the evaluation indicator of the weeding robot's multi-machine cooperative operation, while the extension evaluation is used to assess the robot's multi-machine cooperative operation level and identify the factors that influence its efficiency. In this work, the weeding situation was simulated, and the data were evaluated to determine the important elements influencing the efficiency of each weeding robot group's multi-machine cooperative operation. This model can provide valuable insights and strategies for evaluating and optimizing multi-agent collaborative activity.