Abstract
Due to the increasing global demand for food and shortage of agricultural labor, researchers have focused on developing new technologies, capable of increasing production yields and farming efficiency. Since the repetitive harvesting task is labor-intensive, a robotic harvesting system is one of the promising solutions. To enable precise robotic harvesting, it is important to determine a path for the robot that avoids damaging the crops and other obstacles such as unripe crops, leaves, and stems. In this paper, we present a probabilistic costmap classified according to obstacle type and use it in an RRT-star path planning algorithm. The proposed path planning algorithm has two main features: a biased sampling method based on a probabilistic costmap and a costmap-based collision detector. We have validated the proposed path planning method in the scenario of cucumber harvesting tasks using a two-dimensional planar robot.
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