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

Navigational Path-Planning For All-Terrain Autonomous Agricultural Robot