Motion planning and control for selective harvesting of soft fruit

This PhD is concerned with planning and control of a fruit harvesting robot. This PhD aims to explore state-of-the-art data-driven approaches combined with the analytical methods of motion planning and motion control allowing safe and efficient movements of the robot to push (1) occluding pieces, such as unripe fruits in a strawberry cluster or leaves, to improve the perception (e.g. fruit detection and localisation), and also unripe fruit to reach a ripe one.

Motion planning/control in conventional robotic setup involve collision avoidance as hard constraints to be satisfied during robot movements. However, in challenging situations, such as fruit picking or highly cluttered environments, feasible collision-free robot motions may not be possible. Hence, the robot needs to interact with its environment to change the configuration of the objects in the scene. The new objects configuration should allow occlusion-free perception and collision-free reach-to-pick movements for the robot end-effector.

Conventional motion planning and control limit us to cope with high nonlinearity involved in interactions between the robot and its environments. This PhD will investigate the use of novel data-driven approaches for interactive movement planning and control of the selective harvesting robot. The PhD will study a range of available state-of-the-art (SOTA) data-driven methods and make advancements on SOTA approaches and adapt them to strawberry picking.



The PhD will not only benefit from the training provided by the Agriforwards CDT but also will exploit the available online resources on data-driven approaches, e.g. Deep NN, Reinforcement Learning.

The Risks of this PhD study include (prob 0-3):

Limited access to the lab due to restriction imposed by the University because of COVID19 (prob 2, impact 2); mitigation: plan the development of the work using simulation where possible and use lab equipment to validate and run a small number of experiments.

Self-isolation because of being exposed to COVID19 virus (prob 2 impact 3); mitigation: the student can work from home using the simulation environment.

Missing harvesting season (prob 3 impact 3); mitigation: (I) working with a simulation environment, (ii) working with toy strawberry in the lab and (iii) work in greenhouses provided by

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.