  The shortage of workforce and increasing cost of maintenance has forced many
farm industrialists to shift towards automated and mechanized approaches. The
key component for autonomous systems is the path planning techniques used.
Coverage path planning (CPP) algorithm is used for navigating over farmlands to
perform various agricultural operations such as seeding, ploughing, or spraying
pesticides and fertilizers. This report paper compares novel algorithms for
autonomous navigation of farmlands. For reduction of navigational constraints,
a high-resolution grid map representation is taken into consideration specific
to Indian environments. The free space is covered by distinguishing the grid
cells as covered, unexplored, partially explored and presence of an obstacle.
The performance of the compared algorithms is evaluated with metrics such as
time efficiency, space efficiency, accuracy, and robustness to changes in the
environment. Robotic Operating System (ROS), Dassault Systemes Experience
Platform (3DS Experience), MATLAB along Python were used for the simulation of
the compared algorithms. The results proved the applicability of the algorithms
for autonomous field navigation and feasibility with robotic path planning.
