Deployment of Autonomous Vehicles in Agricultural and using Voronoi Partitioning

There will be nine billion people on the planet by the year 2050, according to most estimates, thus agricultural output must rise steadily. In order to deal with the increasing population, agricultural chores must be mechanized and automated. The autonomous and safe navigation of ground robots has been one of the most difficult challenges in their development over the past decade. With the emergence of the “smart city” movement, automated vehicles have become a hot issue. Authorities and administrators are unprepared to deal with the anticipated disruption of autonomous vehicles, which might potentially replace traditional transportation. We don't yet know how new capabilities will disrupt existing systems and what policy solutions will be necessary to counteract this disruption. Autonomous vehicles have a variety of advantages and disadvantages that must be considered. One such challenge is its path planning. Autonomous vehicle path planning in dynamic situations is a critical yet difficult issue because of the limitations imposed by vehicle dynamics and the presence of other vehicles. Vehicle trajectories include a variety of operations, including lane holding, lane shift, ramp merging, and crossing the street at an intersection. In order to operate autonomously, a mobile platform must perform a variety of tasks such as localization and mapping, as well as motion control and path planning. We 'II look at a bunch of different ways path planning may be put to use in farming in this post. This paper, proposed a Voronoi based partitioning algorithm which is used to find the optimal path for the intelligent vehicles. The Minimum Spanning Tree is computed and the problem is posed as a Travelling Salesperson Problem. The solution for the problem is obtained by solving using various greedy approaches. The results are compared while using various algorithms for the results obtained through the proposed partitioning algorithm.