If the field plot shape is not rectangular and if it contains obstacles, the coverage path planning
problem is hard to solve for a non-omnidirectional machine. Scientists have developed several
algorithms to solve this coverage path planning problem, but all of them have pros and cons. If
the machines were omnidirectional and turning times were decreased to insignificant, the
problem would be quite easy to solve using known robotic path planning methods. Traditional
agricultural machines, like tractors, tractor-trailer combinations, self-propelled harvesters and
other man-driven machines are slow to turn at headlands. This is the most differentiating
property of the problem formulation compared to traditional robotic coverage path planning,
which has dealt mainly with omnidirectional kinematics. In this article two different algorithms
are presented to solve the coverage path planning problem for agricultural machines.
The first algorithm is a higher level algorithm to split a complex shaped field plot to smaller
parts is presented. The higher level splitting algorithm is presented in detail in this article. The
algorithm can handle any field, including obstacles. The algorithm is based on trapezoidal split,
merge and search. The algorithm is suited to any kind of vehicle, which is described with a few
parameters, like working width and turning time function. In the latest version, the required
headlands are generated automatically and there is also a possibility to define regional
restrictions as forbidden driving directions. With this formulation it is possible to take into
consideration the previous operations, under drains and steep gradients.
The second algorithm utilizes bottom-to-top approach. It is designed for real-time usage and it
solves the problem recursively: the operated area is removed from the field and the algorithm is
repeated until the whole field plot is completed. In the development phase of algorithm, a
simulator has been utilized. The underlying idea is to calculate the efficiency for all possibilities
to make one trip around the field and to select the best one. It is assumed that every new swath is
side-by-side to the some previous one or to the boundary of the field plot. However, even if the
underlying idea is simple, the search space explodes when the number of corners of the field plot
raises and heuristics is needed in order to restrict the number of possibilities without losing
optimality. The algorithm is suited to any kind of vehicle, which is described with a few
parameters like working width and minimum turning radius. Preliminary results are very
promising and are presented in the article.