Robotic manipulation strategies for plant tissue sampling

Overview

This solution offers an advanced robotic manipulation system tailored for precise and efficient plant tissue sampling in agriculture. By leveraging machine learning-driven detection and segmentation of plant components, combined with 6DoF pose estimation, this technology enables strategic planning of robotic manipulation. The system is designed to handle the complexities of plant interaction, such as variances in geometry and occlusions, through multimodal sensing (e.g., image, depth, force). This approach aims to replicate the visuo-tactile skills of humans, enhancing the accuracy and efficiency of agricultural tasks like tissue sampling.

Technical specifications

• Machine Learning Algorithms: Utilizes ML-driven algorithms for detecting and segmenting individual plant components.
• 6DoF Pose Estimation: Estimates the precise position and orientation of plant parts to inform manipulation strategies.
• Multimodal Sensing: Integrates image, depth, and force data to enable robust plant interaction.
• Robotic Manipulation Strategies: Develops and executes strategies for tasks such as grasping and separating plant parts.
• Cognitive Robotics: Combines industrial manufacturing experience with agricultural robotics to adapt to variances and unexpected events.

Technology readiness level

Currently at TRL 4, this technology has been validated in laboratory settings with known targets and is progressing towards real-world demonstrations. Future validation includes testing with various plant species and deploying manipulation strategies in controlled environments.