Robust Robotic Fleet Management for Warehouse Operations

Scientific background:
The demand for click-and-deliver e-commerce services for food and grocery has increased. This, in turn, has increased the demand for deploying robotic-fleets in warehouses. There are very commercial players in the field with a feasible commercial solution. In literature, researchers address either the multi-robot task allocation, ignoring the path planning or the multi-robot path planning, ignoring the task planning approaches. However, simultaneously addressing these can ensure efficient collaborations between the robots in sharing the workload of different orders and efficient collision-free multi-robot navigation. There is also a high demand for performance guarantees if robots fail during their operation or if they end up in congested situations causing delays in fulfilling their assigned tasks. This project will explore the development of robust robotic fleet management algorithms with performance guarantees for robustness while deploying in a warehouse.

Research methodology:
The student will investigate (i) clustering of tasks based on the subtasks (items in each order), (ii) a hybrid centralised-decentralised approach for allocation of clusters of pick and delivery, and replenishment tasks preferably using market-based approaches and (iii) associated collision-free and time and energy efficient multi-robot path planning (MRPP) using prioritised multi-robot path planning with time-windowed reservation tables. The MRPP component may also be addressed in a hybrid manner to give robustness guarantees and to ensure scalability to a large number of robots operating in large environments.

Training:
Forwarding the thoughts and vision from a single robot's perspective to those of a fleet of coordinated robots
Understanding of mathematical optimisation problem formulation
Working on physical robotic platforms
Developing and working on simulated environments
Programming and developing algorithms
Understanding and incorporating industry partner's requirements into the research
Academic writing and publishing
Dissemination of research contributions to a wide range of audience

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.