Flexible Hybrid Electronics (FHE) for robotic skin applications

Flexible(printable) electronics currently has applications in sensors and energy storage devices but is severely limited by the mismatch between components. Only a few 'systems' were build based on printable electronics, due to the hardship of producing high-performance circuits. On the contrary, the classic silicon electronics enables for high performance but lacks the flexible substrate. Therefore, Flexible Hybrid Electronics can be used to get the best of both worlds. The technology relies on the use of the silicon ICs for a high-performance component of the circuit like signal processing, power management, or computation, while the sensing can be done by large-area, flexible printed electronics. Therefore the proposal is to develop a system for electronic skin, which relies on a large sensing area build-out of flexible electronics, and readout and processing part realized by traditional ICs. The skin would consist of a square array of sensors, which can be read from similarly as LCD screens are refreshed. The main controller would iterate over addresses or sensor cells and connect them to the conditioning circuit.

While a flexible array of pressure sensors are available on the market, their price goes in thousands of pounds for even simple systems. Moreover, their resolution is lacking, and they offer only one type of sense. On top of that, the connection between the sensor and conditioning electronics can be easily broken when the sensor is bent or twisted. When printing the sensor part of the system, various types of sensors could be combined, like in the case of human skin. There is a possibility of using both resistive and capacitive sensors for pressure, with resistive sensors giving a fast response, while capacitive sensing giving temperature stability. On top of that, temperature sensors, light, humidity, material stress, and more. Moreover, sensors assessing the structural integrity of the skin could be added. Such a system could be cheaply realized using additive-only processes, which work by applying semiconductor and dielectric layers with a screen-printer. Such processes, with interconnection to classic silicon ICs, are commercially available eg. NextFlex. Open source processes, presented by researchers from Singapore exist, with channel lengths as small 100 um presented.

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.