The augmented agronomist, Synthesis of AI, ML and robotics to assist decision support
Lead Research Organisation:
University of Lincoln
Department Name: School of Computer Science
Abstract
With recent advances in Artificial Intelligence and Machine Learning and maturity gained in many robotic applications and domains, this project sets out to provide agronomist with dedicated technological support in assessment and decision making. Adopting innovative paradigms already successfully deployed in tele-medicine and -care, a mobile robotic "proxy", equipped with multi-modal sensing to directly facilitate visual as well as multi-modal (e.g. NIR, moisture, ...) inspection, will be developed and field-tested in the context of soft fruit production. Objectives of the project are
Shared Control and Assisted Assessment from a Mobile Robotic Platform,
Integration of Automated Diagnosis Employing Multi-Modal Sensing,
Robotic Telepresence facilitated through Adaptive Augmented/Virtual Reality Interfaces
Consequently, decision support and outcomes from the automated analysis, as well as control-relevant information are provided by means of virtual and augmented reality, offering an immersive experience and fluid shared control and assessment for the operator. The operator is in a close loop with the system, despite their remote location, enabling them to effectively assess the situation and decide on interventions quickly with all information available at hand. The project is closely linked with the RASberry project (https://rasberryproject.com/) and will have access to its software and hardware resources to minimise risks and maximise synergies.
Shared Control and Assisted Assessment from a Mobile Robotic Platform,
Integration of Automated Diagnosis Employing Multi-Modal Sensing,
Robotic Telepresence facilitated through Adaptive Augmented/Virtual Reality Interfaces
Consequently, decision support and outcomes from the automated analysis, as well as control-relevant information are provided by means of virtual and augmented reality, offering an immersive experience and fluid shared control and assessment for the operator. The operator is in a close loop with the system, despite their remote location, enabling them to effectively assess the situation and decide on interventions quickly with all information available at hand. The project is closely linked with the RASberry project (https://rasberryproject.com/) and will have access to its software and hardware resources to minimise risks and maximise synergies.
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/S507453/1 | 01/12/2018 | 30/11/2022 | |||
2155898 | Studentship | BB/S507453/1 | 01/12/2018 | 30/11/2022 | George Onoufriou |