Maize is one of the major food crops in China. Traditionally, field operations are done by manual labor, where the farmers are threatened by the harsh environment and pesticides. On the other hand, it is difficult for large machinery to maneuver in the field due to limited space, particularly in the middle and late growth stage of maize. Unmanned, compact agricultural machines, therefore, are ideal for such field work. This paper describes a method of monocular visual recognition to navigate small vehicles between narrow crop rows. Edge detection and noise elimination were used for image segmentation to extract the stalks in the image. The stalk coordinates define passable boundaries, and a simplified radial basis function (RBF)-based algorithm was adapted for path planning to improve the fault tolerance of stalk coordinate extraction. The average image processing time, including network latency, is 220 ms. The average time consumption for path planning is 30 ms. The fast processing ensures a top speed of 2 m/s for our prototype vehicle. When operating at the normal speed (0.7 m/s), the rate of collision with stalks is under 6.4%. Additional simulations and field tests further proved the feasibility and fault tolerance of our method.

The separate control method of flippers and the movement of the mass center makes the active articulated tracked robot unable to realize higher-level motion and difficult to adapt to rough and complex obstacle terrain. In this paper, a new design method of distributed autonomous obstacle traversal controller for the novel six-track robot is proposed. The controller establishes a unified control framework that includes all degrees of freedom of the robot so that the center of mass tracking error and flipper motion tracking error can converge simultaneously to achieve obstacle traversal independently of specific terrain or tasks. First, the forward kinematics model and differential kinematics model of tracked robot are established to generate 3D motion, including flipper angular velocity and body traction velocity. Then, the differential drive robot model is extended into the differential kinematic model to eliminate the slip effect during obstacle traversal. Finally, the feedback control law of the control system and the optimal solution for the singular position of the robot structure are established. In addition, several simulation experiments and physical prototype experiments in different obstacle terrains are executed. In the virtual simulation experiment, the average trajectory error of the flipper is about 0.029 m. In the physical prototype experiment, compared to the manual remote controller and the prior art controller, the average error norm of the center of mass is reduced by 40.7% and 13.5%, respectively; the maximum slip norm is reduced by 34.6% and 19.9%, respectively; and the obstacle crossing time is reduced by 21.3% and 9.3%, respectively, and they validate the accuracy and effectiveness of the designed controller.