Abstract
Due to the narrow row spacing of corn, the lack of light in the field caused by the blocking of branches, leaves and weeds in the middle and late stages of corn growth, it is generally difficult for machinery to move between rows and also impossible to observe the corn growth in real time. To solve the problem, a robot for corn interlines information collection thus is designed. First, the mathematical model of the robot is established using the designed control system. Second, an improved convolutional neural network model is proposed for training and learning, and the driving path is fitted by detecting and identifying corn rhizomes. Next, a multi-body dynamics simulation software, RecurDyn/track, is used to establish a dynamic model of the robot movement in soft soil conditions, and a control system is developed in MATLAB/SIMULINK for joint simulation experiments. Simulation results show that the method for controlling a sliding-mode variable structure can achieve better control results. Finally, experiments on the ground and in a simulated field environment show that the robot for field information collection based on the method developed runs stably and shows little deviation. The robot can be well applied for field plant protection, the control of corn diseases and insect pests, and the realization of human–machine separation.
Highlights
Accompanying the development of technologies in artificial intelligence and navigation, robots are increasingly being designed and applied to agricultural science, which is considered a most challenging area of human–computer interaction [1]
Aiming at the issue of plant protection while against corn diseases and insect pests, thisthe study designed a robot to collect crop growth information moving in the field and discussed feasibility of a robot to collect crop growth information while moving in the field and discussed the feasibility of using the visual sensor to identify corn rhizomes, so that the robot could avoid obstacles and move by using the visual sensor to identify corn rhizomes, so that the robot could avoid obstacles and move itself
Based on Faster region-CNN (R-CNN), a method of target detection, which is based on the visual by itself
Summary
Accompanying the development of technologies in artificial intelligence and navigation, robots are increasingly being designed and applied to agricultural science, which is considered a most challenging area of human–computer interaction [1]. Regarding a plant protection robot, its movement mechanism can be mainly divided into two types: a wheel type [2] and a track type [3,4]. Both have their own adaptive environment, respectively. To respond to the different functional requirements for the robot, the design of different structures are needed, and many scholars have carried out the research on the grasping mechanism of the robot, including the design and development of a mechanical arm for a transplanter to process paper can seedlings [7], and the design of a stable and reliable grabbing mechanism [8] for some agricultural product bags, such as tight packing, large deformation and easy damage. Certain achievements have been made in the structural design of agricultural robots in grasping, moving and other motions
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