Low-code software development is an emerging research topic in software engineering. Research in this area has investigated the development of software platforms that allow non-technical people to develop fully functional application software without having to make use of a general-purpose programming language. The scope of most low-code development platforms, however, has been limited to create software-only solutions for business processes automation of low-to-moderate complexity.

Programming of robot tasks still relies on dedicated personnel with special training. In recent years, the emergence of digital twins, block-based programming languages, and collaborative robots that can be programmed by demonstration, has made a breakthrough in this field. However, existing solutions still lack the ability to address variability for programming logistics and manipulation tasks in an everchanging environment.

Current low-code development platforms do not support robotic systems. The extensive use of hardware components and sensorial data in robotics makes it challenging to translate low-level manipulations into a high-level language that is understandable for non-programmers. In this project we will tackle this by constraining the problem focusing on the spatial dimension and by using machine learning for adaptability. Therefore, the first research question we want to investigate in this project is whether and how the low-code development paradigm can support robot programming of spatial logistic task in indoor environments. The second research question will address how to apply ML-based methods for remapping between high-level instructions and the physical world to derive and execute new task-specific robot manipulation and logistic actions.

Therefore, the overall aim of this project is to investigate the use of low-code development for adaptive and re-configurable robot programming of logistic tasks. Through a case study proposed by ER, the project builds on SDU’s previous work on domain-specific languages (DSLs) to propose a solution for high-level programming of the 3D spatial context in natural language and work on using machine learning for adaptable programming of robotic skills. RUC will participate in the project with interaction competences to optimize the usability of the approach.

Our research methodology to solve this problem is oriented towards design science, which provides a concrete framework for dynamic validation in an industrial setting. For the problem investigation, we are planning a systematic literature review around existing solutions to address the issues of 3D space mapping and variability of logistic tasks. For the design and implementation, we will first address the requirement of building a spatial representation of the task conditions and the environment using external sensors, which will give us a map for deploying the ER platform. Furthermore, to minimizing the input that the users need to provide to link the programming parameters to the physical world we will investigate and apply sensor-based user interface technologies and machine learning. The designed solutions will be combined into the low-code development platform that will allow for the high-level robot programming.

Finally, for validation the resultant low-code development platform will be tested for logistics-manipulation tasks with the industry partner Enabled Robotics, both at a mockup test setup which will be established in the SDU I4.0 lab and at a customer site with increasing difficulty in terms of variability.

Making it easier to program robotic solutions enables both new users of the technology and new use cases. This contributes to the DIREC’s long-term goal of building up research capacity as this project focuses on building the competences necessary to address challenges within software engineering, cyber-physical systems (robotics), interaction design, and machine learning.

Scientific value
The project’s scientific value is to develop new methods and techniques for low-code programming of robotic systems with novel user interface technologies and machine learning approaches to address variability. This addresses the lack of approaches for low-code development of robotic skills for logistic tasks. We expect to publish at least four high-quality research articles and to demonstrate the potential of the developed technologies in concrete real-world applications.

Capacity building
The project will build and strengthen the research capacity in Denmark directly through the education of one PhD candidate, and through the collaboration between researchers, domain experts, and end-users that will lead to R&D growth in the industrial sector. In particular, research competences in the intersection of software engineering and robotics to support the digital foundation for this sector.

Societal and business value
The project will create societal and business value by providing new solutions for programming robotic systems. A 2020 market report predicts that the market for autonomous mobile robots will grow from 310M DKK in 2021 to 3,327M DKK in 2024 with inquiries from segments such as the semiconductor manufacturers, automotive, automotive suppliers, pharma, and manufacturing in general. ER wants to tap into these market opportunities by providing an efficient and flexible solution for internal logistics. ER would like to position its solution with benefits such as making logistics smoother and programmable by a wide customer base while alleviating problems with shortage of labor. This project enables ER to improve their product in regard to key parameters. The project will provide significant societal value and directly contribute to SDGs 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation).

In conclusion, the project will provide a strong contribution to the digital foundation for robotics based on software competences and support Denmark being a digital frontrunner in this area.

September 1, 2022 – December 31, 2025 – 3,5 years.

Total budget DKK 7,15 million / DIREC investment DKK 1,97 million

In a traditional, centralized AI architecture, all the technology blocks would be combined in the cloud or at a single cluster (Edge computing) to enable AI. Data collected by IoT, i.e., individual edge-devices, will be sent towards the cloud. To limit the amount of data needed to be sent, data aggregation may be performed along the way to the cloud. The AI stack, the training, and the later inference, will be performed in the cloud, and results for actions will be transferred back to the relevant edge-devices. While the cloud provides complex AI algorithms which can analyse huge datasets fast and efficiently, it cannot deliver true real-time response and data security and privacy may be challenged.

When it comes to Embedded AI, where AI algorithms are moved to the edge, there is a need to transform the foundation of the AI Stack by enabling transformational advances, algorithmic agility and distributed processing will enable AI to perceive and learn in real-time by mirroring critical AI functions across multiple disparate systems, platforms, sensors, and devices operating at the edge. We propose to address these challenges in the following steps, starting with single edge-devices.

1. Tiny inference engines – Algorithmic agility of the inference engines will require new AI algorithms and new processing architectures and connectivity. We will explore suitable microcontroller architectures and reconfigurable platform technologies, such as Microchip’s low power FPGA’s, for implementing optimized inference engines. Focus will be on achieving real-time performance and robustness. This will be tested on cases from the industry partners.
2. µBrains – Extending the edge-devices from pure inference engines to also provide local learning. This will allow local devices to provide continuous improvements. We will explore suitable reconfigurable platform technologies with ultra-low power consumption, such as Renesas’ DRP’s using 1/10 of the power budget of current solutions, and Microchip’s low power FPGA’s for optimizing neural networks. Focus will be on ensuring the performance, scheduling, and resource allocation of the new AI algorithms running on very resource constrained edge-devices.
3. Collective intelligence – The full potential of Embedded AI will require distributed algorithmic processing of the AI algorithms. This will be based on federated learning and computing (microelectronics) optimized for neural networks, but new models of distributed systems and stochastic analysis, is necessary to ensure the performance,
   prioritization, scheduling, resource allocation, and security of the new AI algorithms—especially with the very dynamic and opportunistic communications associated with IoT.

The expected outcome is an AI framework which supports autonomous discovery and processing of disparate data from a distributed collection of AI-enabled edge-devices. All three presented steps will be tested on cases from the industry partners.

Deep neural networks have changed the capabilities of machine learning reaching higher accuracy than hitherto. They are in all cases on learning from unstructured data now the de facto standard. These networks often include millions of parameters and may take months to train on dedicated hardware in terms of GPUs in the cloud. This has resulted in high demand of data scientists with AI skills and hence, an increased demand for educating such profiles. However, an increased use of IoT to collect data at the edge, have created a wish for training and executing deep neural networks at the edge rather than transferring all data to the cloud for processing. As IoT end- or edge-devices are characterized by low memory, low processing power, and low energy (powered by battery or energy harvesting), training or executing deep neural networks is considered infeasible. However, developing dedicated accelerators, novel hardware circuits and architectures, or executing smaller discretized networks may provide feasible solutions for the edge.

The academic partners DTU, KU, AU and CBS, will not only create the scientific value from the results disseminated through the four PhDs, but will also create important knowledge, experience, and real-life cases to be included in the education, and hence, create capacity building in this important merging field of embedded AI or AIoT.

The industry partners Indesmatech, Grundfos, MAN ES, and VELUX are all strong examples of companies who will benefit from mastering embedded AI, i.e., being able to select the right tools and execution platforms for implementing and deploying embedded AI in their products.

• Indesmatech expect to gain leading edge knowledge about how AI can be implemented on various chip processing platforms, with a focus on finding the best and most efficient path to build cost and performance effective industrial solutions across industries as their customers are represented from most industries.
• Grundfos will create value in applications like condition monitoring of pump and pump systems, predictive maintenance, heat energy optimization in buildings and waste-water treatment where very complex tasks can be optimized significant by AI. The possibility to deploy embedded AI directly on low cost and low power End and Edge devices instead of large cloud platforms, will give Grundfos a significant competitive advantage by reducing total energy consumption, data traffic, product cost, while at the same time increase real time application performance and secure customers data privacy.
• MAN ES will create value from using embedded AI to predict problems faster than today. Features such as condition monitoring and dynamic engine optimization will give MAN ES competitive advantages, and
  the exploitation of embedded AI together with their large amount of data collected in the cloud will in the long run create marked advantages for MAN ES.
• VELUX will increase their competitive edge by attaining a better understanding of the ability to implement the right level of embedded AI into their products. The design of new digital smart products with embedded intelligence, will create value from driving the digital product transformation of VELUX.

January 1, 2022 – December 31, 2024 – 3 years.

Total budget DKK 22,5 million / DIREC investment DKK 6,54 million.

The research aim of the project is the development of AI methods and tools that enable industry to develop new solutions for automated image-based quality assessment. End-to-end learning of features and representations for object classification by deep neural networks can lead to significant performance improvements. Several recent mechanisms have been developed for further improving performance and reducing the need for manual annotation work (labelling) including semi-supervised learning strategies and data augmentation.

Semi-supervised learning  combines generative models that are trained without labels (unsupervised learning), application of pre-trained networks (transfer learning) with supervised learning on small sets of labelled data. Data augmentation employs both knowledge based transformations, such as translations and rotations and more general learned transformations like parameterised “warps” to increase variability in the training data and increase robustness to natural variation.

The scientific value of the project will be new methods and open source tools and associated knowledge of their performance and properties in an industrial setup.

For capacity building the value of the project is to educate one PhD student in close collaboration with FOSS – the aim is that the student will be present at FOSS at least 40% of the time to secure a close integration and knowledge exchange with the development team at FOSS working on introducing EyeFossTM to the market. Specific focus will be on exchange at the faculty level as well; the aim is to have faculty from DTU Compute present at FOSS and vice-versa for senior FOSS specialists that supervise the PhD student. This will secure better networking, anchoring and capacity building also at the senior level. The PhD project will additionally be supported by a master-level program already established between the universities and FOSS.

Specifically, the project aims to provide FOSS with new tools to assist in scaling the market potential of the EyeFossTM from its current potential of 20m EUR/year. Adding, in a cost-efficient way, applications for visual inspection of products like maize, rice or coffee has the potential to at least double the market potential. In addition, the contributions will be of generic relevance to companies developing image-based solutions for food quality/integrity assessment and will provide excellent application and AI integration knowledge of commercial solutions already on-the-market to other Danish companies.

The project involves the research themes of AI (WS2) and CyPhys (WS6) of DIREC.

October 1, 2020 – September 31, 2024 – 3.5 years

Total budget DKK 3,91 million / DIREC investment DKK 1,90 million

The research aim of the project is methods and tools that enable industry to develop new solutions with accurate AI-based maintenance predictions on edge-based software platforms.

The resulting applications must be deployed to collect and process large amounts of data locally. This data feeds high accuracy predictive models deployed at the edge, adapted to changing local conditions, and maintained with minimum intervention from operators.

These models should cover abstractions allowing the understanding of relevant dependencies in the data. The key research problem is to devise architectures and solutions that scale to the entire fleets of equipment with accurate AI predictions.

This also requires that resources in terms of processing power, storage and communication, are optimised in order to obtain low-power and real-time performance, leading to a resource efficiency vs prediction accuracy trade-off. The project will establish a bridge to enable Danish companies to develop and use AI-based predictive maintenance within several domains.

The scientific value of the project is new methods and tools and associated knowledge of their performance and properties in field tests. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI and IoT.

For capacity building the value of the project is to educate 3 PhD students (including 1 Industrial PhD) and 1 Post Doc in close collaboration with industry. The open source availability of general project outcomes and industry collaboration enable several exploitation paths. In addition, for the master-level the projects will offer an industry program to 15 students at 3 universities.

The business and societal value is on a national level estimated to a 14-19 billion potential for the Danish private sector. In the project we target both the medical, robotic, industrial and energy sectors. These are Danish frontrunners in adopting the technology and creating inspiration for wider adoption by the Danish private sector. For the public sector enabling equipment with higher operation efficiency will positively impact the efficiency of the sector.

Within the area of AI the project will research and develop AI models applicable to predictive maintenance. Furthermore, research will consider methods for handling limitations on training data among others due to data ownership restrictions and confidentiality. A final aspect is work on AI models that can adapt to different edge conditions including available processing power and timing deadlines.

Within the area of Cyber-physical systems the project will research and develop software architectures and platforms for edge-based execution of AI models. The outcomes should enable AI models to adapt to changing local conditions, and be maintained with minimum intervention from operators. The key research problem is to devise architectures and solutions that scale to the entire fleets of equipment with accurate AI predictions. This requires methods that optimize resources in terms of processing power, storage and communication in order to obtain low-power and real-time performance, leading to a resource efficiency vs prediction accuracy trade-off.

October 1, 2020 – September 31, 2024 – 3.5 years

Total budget DKK 12.24 million / DIREC investment DKK 6.3 million