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Agriforwards CDT

 

Agriforwards CDT programme has started in 2019. From that time we have been training 3 cohorts and are looking forward to recruit two more.

Our student group is trully international and they have different backgounds in Engineering, Plant scineces and Computer Sciences.

We offer fully funded studentships for eligible UK students. We also encourage overseas candidated to apply as every year we have a small number of fully funded international studentships avaialble.

Below is the list of our currnet students and their reseach topics and publications:

Grzegorz Sochacki - Cohort 1 (2019-2023)

Research Interests

Soft robotics with a focus on sensing.

PhD Project: Low-Cost Dextrous Robots for Food and Tool Handling

While the conventional robots are very successful in well-structures and predicable tasks and environments such as automobile assembling factories, they still underperform in unstructured and/or less-predictable tasks such as food preparation, cooking and associated tasks in domestic kitchens. To tackle these challenges, this project aims to develop soft robot manipulators that can perform some of these tasks to help simplify complex operations in kitchens in ordinary houses, possibly in cooperation with human users.

One of the main research drivers of this project is the use of sensorised soft robotic grippers that are able to handle variations of objects such as fruits, vegetables, plates, cooking tools etc. (water taps and dish washers) based on the guidance provided by computer vision. There are four main technological challenges in this domain as follows. First the closed-loop control of soft gripper interacting with a large variety of objects is a fundamental challenge. The use of mechanically adaptable structures needs to be utilised for grasping of a large variety of uncertain objects, while sensoring such systems needs a well-thought integration of soft tactile sensors into advanced feedback control processes, including the strategies to make the entire hardware setup reliable and economical. Second, the use of machine learning for visual recognition of variations of household objects is still a significant challenge particularly in unstructured environment and task, such as cleaning of dishes. And third, the interactions with human users in such an advanced platform are unsolved. What is the framework of human interfaces for complex robots for easy programming and teaching? How can humans give feedback to learning cooking robots? What is the framework of health and safety for such advanced robotic systems in household? The fourth is to address technological challenges allowing cost reduction in order to make robotic solutions more affordable and accessible. By developing this cutting-edge robotics platform in household, we will explore these fundamental questions in this project. The outcome of this project is also contributing to the automation of complex food manipulation tasks in the Agri-Food industry at large.

Publications

  • Sochaki, G., Iida, F. and Hughes, J. (2021) ‘Compliant Sensorized Testing Device to Provide a Model-Based Estimation of the Cooking Time of Vegetables’, 16th International Conference on Intelligent Autonomous Systems, doi:10.17863/CAM.66275.
  • Sochacki, G., Hughes, J., Hauser, S., and Iida, F. (2021) ‘Closed-Loop Robotic Cooking of Scrambled Eggs with a Salinity-based ‘Taste’ Sensor’, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 594-600, doi:10.1109/IROS51168.2021.9636750.
  • Sochacki, G., Hughes, J., and Iida, F. (2022) ‘Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables’, Intelligent Autonomous Systems 16 (IAS 2021), doi:10.1007/978-3-030-95892-3_17.
  • Sochacki, G., Abdulali, A., and Iida, F. (2022) ‘Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking’, Frontiers in Robotics and AI-Bio-Inspired Robotics, doi:10.3389/frobt.2022.886074.
Elijah Almanzor - Cohort 2 (2020-2024)

Research Interests

Intelligent soft robots for soft/delicate harvest picking.

PhD Project: Automation and Robotization of the Planting of the ‘Jersey Royal’ Potatoes

The agricultural industry in Jersey faces a considerable technological challenge in the planting of their main product, Jersey Royal new potatoes, due to the lack of available manual labour from Brexit, increase in wages as well as the Covid19 pandemic.

Research into robotic technologies for low-cost rapid handling of the seed potatoes from storage to soil is explored in the project. Low-cost robotic arms with suitable grasping end-effectors and machine intelligence will be developed and tested with reasonable speed, accuracy, and reliability. Exploration of minimalistic solutions for locomotion such that the planting robot can be mobilised will also be undertaken.

Haihui Yan - Cohort 2 (2020-2024)

Research Interests

Mechanism design of robotic systems, vision and perception systems.

PhD Project: 3D Printing Soft Robotic Grippers for Automated Strawberry Harvesting

In 2019, strawberries were the second most produced fruit in England dominating ~21% (141.6 thousand tons) of the market, but achieved the highest fruit value of 2.46 million pounds/thousand ton. However, strawberries are commonly harvested by hand, which is a very labour-intensive job. Moreover, there has been a consistent decline in the number of available pickers, in the autumn of 2019, UK farmers reported a 30 percent shortage in pickers. Therefore, there is an urgent need for a highly effective and smart or human-like strawberry harvesting design to meet this gap. The main challenges to strawberry robotic harvesting are bruising, abrasions and other mechanical damages of strawberries. To address these challenges a new solution is proposed, which involves the design of a gripper structure, allowing highly efficient harvesting of strawberries with no mechanical damages.

General rigid robotic grippers have been designed with pneumatic and hydraulic gripper solutions. However, it is often difficult to control the gripper precisely as they are rigid and non-compliant. Soft robotics offers more compliancy and flexibility. They are also 3D printable with more possible solutions.

3D printing of soft robotics has been extensively explored recently. These include a range of soft materials including Liquid crystal elastomers (LCE). They have a lot of potential to overcome current grippers’ problems. They are more flexible, lightweight and they are 3D printable. These characteristics maximise the potential these materials to produce a flexible and compliant gripper suitable for bruise-free strawberry harvesting.

This project will involve the design of a novel 3D printed robotic gripper targeted for strawberry harvesting. This will involve producing a specification for strawberry harvesting and using it to produce a design.

Haris Matsantonis - Cohort 2 (2020-2024)

Research Interests

Robot vision and human-robot interaction, with particular focus on geometric algebra.

PhD Project: Design and implementation of a machine vision system to promote precision agriculture innovation using novel Geometric Algebra techniques.

The aim of the project is to develop state of the art autonomous robots (articulated or mobile) for agricultural production systems. This is a complex problem given the varying parameters of any given situation: changing workspace, the robot’s kinematic constraints, variation in the input sensor data.

Publications:

Anthony Lasenby, Joan Lasenby, Charalampos Matsantonis. Reconstructing a Rotor from Initial and Final Frames using Characteristic Multivectors: with applications in Orthogonal Transformations: Version 2. Authorea. February 07, 2022.
DOI: 10.22541/au.164423636.68135579/v1

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Lasenby, ALasenby, JMatsantonis, CReconstructing a rotor from initial and final frames using characteristic multivectors: With applications in orthogonal transformationsMath Meth Appl Sci2022118. doi:10.1002/mma.8811

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Jack Foster - Cohort 2 (2020-2024)

Research Interests

Deep learning, multi-agent systems and computer vision.

PhD Project: Collaborative Lifelong Learning for Robust Site-Specific Crop Management

Farms are not, in general, homogeneous. As such, the farm-wide treatment and maintenance of crops leads to sub-optimal crop yield or quality. By taking a finer grained approach, crop would receive the necessary care for their needs, rather than the average need of the farm. However, it is impractical for human experts to manually analyse the needs of crop on such a granular scale.

To address this need, we first propose novel machine learning approaches for crop care, such as soil-moisture optimisation via LSTM neural networks. To collect real-time data of the farm a custom sensor system will be developed and several of them deployed to collect relevant environmental data from a small region of the farm.

Finally, to improve the long-term autonomy and overall performance of an agent, a collaborative multi-agent system will be constructed to facilitate the lifelong learning from both an agent’s environment, but also from other agents. This will improve the robustness and performance of agents over long periods of time.

 

William Rohde - Cohort 2 (2020-2024)

Research Interests

Soft robotics, manipulation, human-robot collaboration, interaction.

PhD Project: Autonomous monitoring and control of crop growth as a feedback system

The project will model and control the growth of crops in an agricultural setting. The goal is to enable growers to maximise their harvest, by taking advantage of distributed sensing to optimise the use of fertilisers and of automation for crop management. The impact of the project will be the first direct application of feedback control to plant growth in an agricultural field.

Will will work through four work packages. The student will develop: (i) lettuce growth modelling as an open dynamical system, (ii) feedback control algorithms for crop optimization, (iii) distributed sensing technologies, (iv) automation for growth control. The student will take advantage of the facilities of the Department of Engineering of the University of Cambridge (Control prototyping lab, Agripods within the Observatory for Human-Machine Collaboration) and of the industry partner G’s growers (expertise, extensive databases, sensing technologies, automation).

Bethan Moncur - Cohort 3 (2021-2025)

Research Interests

Food manufacturing, strategic technology management, and industrial sustainability.

 

Garry Clawson - Cohort 3 (2021-2025)

Research Interests

Digital supply chains, manipulation, soft robotics, sensing and perception.

 

Kyle Fogarty - Cohort 3 (2021-2025)

Research Interests

Development of long-term autonomy for robotics, the theoretical development and practical application of computer vision, computer graphics, and machine learning techniques.

 

Paul-David Zuercher - Cohort 3 (2021-2025)

Research Interests

Autonomous systems, immersive technologies and human-robot collaboration.

 

Rachel Trimble - Cohort 3 (2021-2025)
Vijja (Pat) Wichitwechkarn - Cohort 3 (2021-2025)

Research Interests

Vertical farming, urban farming, controlled-environment farming, robotics and automation, computer vision, generalisation in neural networks.