PhD position in robotic grasping and manipulation for fruit picking
Norwegian University of Life Sciences
The RAS-Berry project
The RAS-Berry project will develop autonomous fleets of robots for strawberry production in polytunnels. The project will develop a dedicated mobile platform together with software components for fleet management, long-term operation and safe human robot collaboration in strawberry production facilities. In particular, the candidate will study how to use these robots as a platform for fruit picking.
The project is a collaboration between the Norwegian University of Life Sciences (NMBU) and University of Lincoln, and is looking to employ a total of three postdocs and four PhD students. The successful candidates will have access to state-of-the art research farms that will be equipped with production facilities with industrial standard. The project also has access to a wide variety of agricultural robots with advanced sensors and tools. This equipment is already installed on the research farms and will be made available to the project. There is a strong focus on developing solutions that are robust in realistic scenarios, and extensive field testing is therefore required. In order to coordinate the work of everyone involved in the project, several workshops will be held both in Norway and the UK.
The candidate will study how to use these robots as a platform for fruit picking. The candidate will work closely with experts on machine vision and machine learning, and will implement advanced trajectory tracking and grasping strategies based on 3D-point cloud vision. The fruit detection and grasping strategies will be refined using state of the art machine learning techniques.
The main task of the candidate is to implement fine manipulation in the agricultural environment. The main activities will be related to the following topics:
• Implementation of real-time systems.
• Trajectory planning and control of robotic manipulators.
• Grippers and fine manipulation.
• Grasp planning and grasp refinement using machine learning.
• Visual servoing for robust grasping.
Required Academic qualifications
• A relevant Master degree in Engineering Cybernetics with an average grade B or better as measured in ECTS (European Credit Transfer System) grades.
• Proficiency in robotics and control.
• Proficiency in mathematical modeling, statistical methods.
• Hands-on experience from implementing algorithms on real systems.
• Excellent programming skills
Desired Academic qualifications
• Experience with ROS
• Machine learning, artificial intelligence.
• Construction of robots, hands-on experience in building robotic systems.
Please apply through the following site:
Pål Johan From, Ph.D.
Faculty of Mathematical Sciences and Technology
Norwegian University of Life Science