Development of a motion planning system for an agricultural mobile robot

Describes the development of a motion planning system which plans an optimal work path for an autonomous agricultural vehicle on farm land. The system consists of two parts: a global path planning component and a local motion planning component. The global path planning component works to acquire an optimal work path for a whole field. In this case, the optimal work path is the lowest traveling cost from a start point to a goal point. The local motion planning component acquires the optimal path and plans an optimal control policy on a headland. In this motion planning, an optimal solution is a path with a low traveling cost and soil compaction. These components are implemented with the following algorithms: simulated annealing, Tabu search, genetic algorithm, and reinforcement learning. We solve the optimal path problems on a headland using computer simulation