Accurate control of a low-cost soft robotic arm for automated strawberry picking (Ref: CTP_FCR_2020_5)
Lead Research Organisation:
University of Lincoln
Department Name: School of Computer Science
Abstract
This student is to be registered with University of Lincoln and will be mainly based in the University. The team of supervisors includes Prof Gerhard Neuman (University of Lincoln), Dr Khaled Elgeneidy (University of Lincoln), and Dr Bo Li (NIAB)
The PhD student will work on the development and control of a bespoke soft arm targeted at the strawberry picking application. This is expected to involve embedding of customised flexible sensors to provide additional positional feedback so that accurate closed-loop control can be achieved. The design of the soft arm will consider the potential for delivering picked strawberries to the robot base by utilising an internal passage through the soft arm, to reduce the need for moving the arm back to the base after each pick and hence shortening the picking cycle. The soft arm body can be potentially 3D printed from flexible materials to automate the fabrication process and yield a more consistent output. Machine learning algorithms will be investigated for online learning control for the soft arm, such that variations from the soft materials and fabrication process can be effectively accounted for using experimental data.
The PhD student will work on the development and control of a bespoke soft arm targeted at the strawberry picking application. This is expected to involve embedding of customised flexible sensors to provide additional positional feedback so that accurate closed-loop control can be achieved. The design of the soft arm will consider the potential for delivering picked strawberries to the robot base by utilising an internal passage through the soft arm, to reduce the need for moving the arm back to the base after each pick and hence shortening the picking cycle. The soft arm body can be potentially 3D printed from flexible materials to automate the fabrication process and yield a more consistent output. Machine learning algorithms will be investigated for online learning control for the soft arm, such that variations from the soft materials and fabrication process can be effectively accounted for using experimental data.
People |
ORCID iD |
Philip Johnson (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/V509802/1 | 01/10/2020 | 30/09/2024 | |||
2476449 | Studentship | BB/V509802/1 | 01/10/2020 | 30/09/2024 | Philip Johnson |