This paper presents an optimum harvesting area of a convex and concave polygon for the path planning of a robot combine harvester. A convenient optimum harvesting area for a convex and concave polygon is proposed. The notion is that path planning specifically for a robot combine harvester is required to choose the crop field optimum harvesting area; otherwise, crop losses may occur during harvesting of the field. For a safe turning margin of the robot combine harvester, the surrounding crop near the boundary zone is cut twice or thrice by manual operation. However, this surrounding cutting crop is not exactly straight, and sometimes it is curved or meanders. In addition, path planning with a conventional AB point method in order to take a corner position from the global positioning system by visual observation is a time-consuming operation. A curved or meandering crop is not cut and left in the field during harvesting, and the harvesting area is not optimum. Therefore, a suitable N-polygon algorithm and split of convex hull and cross-point method for determining the optimum harvesting area for path planning are proposed, which reduce the crop losses in the field. The results show that this developed algorithm estimates the optimum harvesting area for a convex or concave polygon field and its corner vertices, takes all crop portions, and reduces crop losses. It is also illustrated that the working path calculated based on the corner vertices minimizes the total operational processing time.

Absolute heading is an important parameter for a robot combine harvester or a robot tracked combine harvester, especially while it is turning, but due to the rapid turning of robot combine harvesters, its inertial measurement unit gives a gyro measurement bias that causes heading drift. Our research goal is to estimate the absolute heading of robot combine harvesters by compensating this gyro measurement bias during non-linear turning maneuvers. A sensor fusion method like the extended Kalman filter combined with the tracked combine harvester dynamic model and sensor measurements was used to estimate the absolute heading of a robot combine harvester. Circular, sinusoidal and concave shapes were used to evaluate the estimated heading produced by the sensor fusion method. The results indicate that the estimated heading is better than measured heading which was calculated from the integration of yaw rate gyro measurements, and the root mean square errors (RMSEs) for estimated headings are smaller than the measured headings. In practics, the target of this paper is thus the estimation of a heading or absolute heading that is bias compensated, and can be further used to calculate the exact crop periphery for automatic path planning of robot combine harvesters.