View Planning for Grape Harvesting Based on Active Vision Strategy under Occlusion

Replacing humans with robots for fruit harvesting is the trend of agricultural automation in the future. However, for grape harvesting robots, locating the picking point becomes a significant challenge in highly occluded environments due to the small fruit stem, which can be entirely obscured by fruit leaves when the observation angle is poor. In the work, a view planner based on an active vision strategy is proposed to address the occlusion problem. It aims to find the picking point by altering the observation perspective of the harvesting robot. The view planning process is achieved through multiple iterations. Each iteration consists of three key steps: randomly generating candidate views, predicting the ideal perspective using a score function, and guiding the robotic arm to change the viewpoint. To evaluate the degree of occlusion, a novel concept of Spatial Coverage Rate Metric (SC) is introduced. Based on this, the score function is improved by incorporating SC and motion cost. Finally, to validate the effectiveness of the planner, we conducted comparative experiments with other advanced view planners on a real grape harvesting robot. The experimental results demonstrate that our method achieves a higher picking success rate with lower computation time.