5 key challenges in robotics and autonomous systems in UK’s food chain

Agri-Food is the UK’s largest manufacturing sector. It supports a food chain with over £ 108 billion in turnover, 3.9 million in a truly international industry, and exports £ 20 billion of manufactured goods. The global food chain is under tremendous pressure from population growth, climate change, migration policy pressures, urban, rural drift populations, and aging world demographics.

Robotics autonomous systems (RAS) and related digital technologies are enabling this critical food chain transformation. Opportunities for the RAS range include: development of field robots supporting workers by transporting payloads and operations such as crop and animal detection, weeding and boiling; integration of autonomous systems in existing farm operating equipment such as tractors; robotic systems for harvesting crops and conducting complex extrusive operations; RAS technology has the potential to be used in the field. It, however, needs to overcome the following challenges.

Fragmentation and Community Expansion:

The UK RAS community in the agri-food sector is currently small and fragmented. This fragmentation hinders the development of a cohesive strategy and limits the sharing of knowledge and resources. To address this, it is crucial to consolidate efforts and expand the community. Creating a unified network can facilitate better collaboration, innovation, and resource allocation, leading to more substantial and impactful advancements in agri-food robotics. Without defragmentation, the sector risks duplication of efforts and slower progress.

Lack of Specific Training Paths:

There is a significant gap in specialized training and education for RAS in the agri-food sector. The absence of dedicated doctoral training centers and specific training paths means there is a shortage of skilled professionals who can drive innovation in this field. Establishing these educational pathways is essential to developing a workforce capable of designing, implementing, and maintaining advanced robotic systems. Investment in education will ensure a steady supply of talent, fostering continuous innovation and addressing future challenges in agri-food automation.

Insufficient Basic Research:

While there has been substantial government investment in high-technology readiness level (TRL) activities, basic research at lower TRLs remains underfunded. This fundamental research is critical for pioneering new technologies that can be later developed into practical applications. By increasing funding and support for basic research in agri-food RAS, the UK can lay a stronger foundation for future innovations. This approach will ensure a pipeline of new ideas and technologies, providing the necessary groundwork for advanced, industry-ready solutions.

Integration and Interoperability Challenges:

The complex integration of various discrete technologies—such as navigation, security operations, grasping and manipulation, and perception—is a significant hurdle. These technologies must not only be developed independently but also integrated seamlessly into comprehensive systems. Large-scale, collaborative projects, often referred to as “moonshot” projects, are needed to tackle these integration challenges. Such projects can bring together diverse expertise and resources, promoting interoperability and ensuring that different technologies work harmoniously together. This large-scale approach is essential for creating robust, reliable RAS systems capable of handling complex agricultural tasks.

Cross-disciplinary Collaboration:

Successful implementation of RAS in agri-food requires close collaboration between RAS experts and professionals from academia and industry. This collaboration is crucial for addressing sector-specific challenges, such as developing crop varieties that are easier for robots to handle. Establishing coordinated networks and integrated research policies can facilitate this collaboration, leading to more targeted and effective innovations. A unified approach, supported by coordinated funding from various research councils and innovation agencies, will streamline efforts and enhance the overall impact of RAS technologies in the agri-food sector.

Research and Innovation Needs

To overcome these challenges, the following areas require focused research and innovation:

  • Development of Robust Platforms: Creating durable, farm-friendly robotic platforms that can operate in diverse agricultural environments.
  • Enhanced Sensing and Perception: Improving the ability of robots to perceive and interpret their surroundings accurately.
  • Planning and Coordination: Developing advanced algorithms for efficient planning and coordination of robotic tasks.
  • Manipulation and Grasping: Innovating new methods for robots to handle and manipulate crops and other agricultural products.
  • Learning and Adaptation: Enabling robots to learn from their experiences and adapt to changing conditions.
  • Interoperability: Ensuring that different robotic systems can work together seamlessly.
    Human/Robot Cooperation: Fostering safe and efficient collaboration between humans and robots.

Technology Adoption and Transition

The adoption of RAS technologies in agriculture will likely occur in measured steps. Farmers and food producers will need solutions that integrate with existing production systems and allow for a gradual transition. This phased approach ensures that both technology and workforce can adapt progressively, maintaining productivity and enhancing job roles. Initial simple collaborative tasks between humans and robots can build trust and familiarity, paving the way for more complex operations and driving overall sector growth.

By addressing these challenges and focusing on the outlined research and innovation needs, the UK can harness the full potential of RAS technologies to transform its agri-food sector, ensuring sustainability, efficiency, and competitiveness in the global market.