Human sensing based on affordable sensors for collaborative robotics in agricultural scenarios

Although most of agricultural robots are considered small/medium size machinery, they still represent a risk of causing injuries to human operators/collaborators, especially in situations when the robots are not aware of the human presence or intentions.

Thus, to accelerate the deployment of robots on industrial-scale farms, it is crucial to develop a reliable human sensing methodology capable to ensure a safe and efficient human-robot interaction. Most of existent solutions for human sensing have been developed for robotic applications in controlled environments or requires expensive sensors (>£10k each). In contrast, this proposal aims to develop a human sensing system based on affordable sensors (<£1k each), making the proposed solution suitable to be adopted by agri-robotic companies as a cost-effective safety solution.

To accomplish this goal, it is necessary to divide the work plan into two parts. The first part is the core of the proposal that focuses on developing the human sensing system. This system will fuse information collected from multiple sensors with different features. The solution will be able to adjust dynamically the human detection performance according to lighting and occlusion conditions, the agricultural environment context, and the robot energy savings requirements. The human sensing will cover not only tracking human position but also infer the human intensions based on motion prediction, human-object interactions, and the action they are performing.

Once the first part was completed, the second part is to close the loop by using the information from the human sensing system to feed the decision-making system of the robot. Thus, robot actions will depend not only on the agricultural task but will consider the human presence and intention inference. The performance of this human-aware navigation will be validated experimentally, demonstrating its potential to be deploy in different robotic platforms and different agricultural environments.

In this project, the student will acquire technical skills such as robot programming, sensors integration, computer vision techniques, machine learning, human-robot interaction, and scientific writing.

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.