NBV Planning for the detection of hidden tomatoes in greenhouses with AgriSEE
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12234Keywords:
agricultural robotics, mobile robots, robots manipulators, trajectory and path planning, map buildingAbstract
Intensive greenhouse agriculture is essential to support global population growth. However, the increasing population demands a transformation of current agricultural systems into more efficient and sustainable models. In this context, automation, particularly robotics, emerges as a key solution to address the challenges of the sector, especially tasks that are monotonous, unhealthy, or hazardous (DDD: Dull, Dirty, Dangerous). This study focuses on implementing a Next Best View (NBV) planning algorithm, specifically SEE++, to generate 3D models using point clouds. A Universal Robots UR3 robotic arm is equipped with an Intel RealSense L515 camera to scan fruits in a simulated greenhouse environment. The proposed methodology dynamically determines the next best view to optimise the scanning process. The results demonstrate the system’s ability to detect tomatoes in dynamic simulation environments, where traditional methods fail to accurately locate partially occluded objects. This work lays the foundation for future applications in automated harvesting.
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Copyright (c) 2025 Fernando Cañadas Aranega, Rowan Border, Jose Luis Blanco Claraco, Jose Carlos Moreno Úbeda
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