So, to that end, a consortium of universities, technology accelerators launched RoboSAPIENS, a project that aims to ensure the industrial robots of the future can readily adapt to changing environments, while maintaining safe and trustworthy collaboration with humans.
According to the latest data published by the International Federation of Robotics, industrial robot installations in Europe were up 24 percent to 84,302 units in 2021. On the cutting edge of this growth are adaptive robotics – or robots that can learn new things without needing reprogramming.
RoboSAPIENS will advance the field of robotic self-adaptation and empower robots with open-ended autonomous software adaptations, allowing them to dynamically respond to unforeseen changes in system structure or environment while maintaining trustworthy collaboration with humans.
Specific objectives for the program include:
Enable open-ended adaptation: Develop control software for robotic open-ended self-adaptation, specifically addressing unprecedented changes in system structure and environmental conditions, including human interactions.
Enhance safety assurance: Advance safety engineering techniques to ensure not only pre-adaptation but also during and post-adaptation safety, incorporating trustworthiness checkers and self-evaluation schemes.
Reduce task uncertainty with deep learning: Utilize Deep Learning (DL) techniques to actively reduce task uncertainty in robotic self-adaptation, ensuring more reliable and reproducible adaptations, with a focus on addressing uncertainties inherent in DL models.
Assure trustworthiness: Develop and apply formal methods to assure the trustworthiness of systems using both DL and computational architectures for robotic self-adaptation, incorporating verification tools and techniques.
“As the EU advances its industrial robotics capabilities, it’s imperative that measures be taken to ensure the safety and trustworthiness of the next generation of robots working alongside humans in a collaborative environment,” said Peter Gorm Larsen, a professor in the department of electrical and computer dngineering at Aarhus University, and the coordinator of the RoboSAPIENS project. “With RoboSAPIENS, we will prepare Europe’s industrial robotics industry for rapid and unforeseen adaptability in system structure and environment, assuring trustworthy collaboration with humans by changing behavior while maintaining and even enhancing performance and safety.”
In addition to Denmark’s Aarhus University, other consortium partners include: Aristotle University of Thessaloniki, AUTH (Greece); Danish Technological Institute, DTI (Denmark); Fraunhofer IFF, Fraunhofer (Germany); University of York, UoY (United Kingdom); University of Antwerp, UA (Belgium); Norwegian University of Science and Technology, NTNU (Norway); PAL Robotics, PAL (Spain); ISDI Accelerator, ISDI (Spain); Simula Research Lab, SRL (Norway).
RoboSAPIENS will elaborate on the current autonomic computing framework, often referred to as Monitor-Analyze-Plan-Execute-Knowledge (MAPE-K), to include adaptive controllers in robotic applications, and reconcile it with stringent safety requirements.
The generation of the adapted controller settings will largely rely on DL and Digital Twin (DT) — or a virtual representation of a physical object — techniques. Automated (virtual and real) tests will deploy to evaluate a safe operation of the robot after adaptation.
Eventually, RoboSAPIENS will work with industrial robotics manufacturers and users to incorporate the adaptive controllers into robotic applications. The main output of the RoboSAPIENS program aims to deliver a demonstrated and validated architecture to reconcile reliability and trustworthiness with open-ended self-adaptation for Europe’s industrial robots.
Industrial Case Studies in Action
The project is already actively undertaking industrial use studies with four manufacturing and logistics companies that use industrial robots.
The first case study involves empowering a robot cell to disassemble electronic consumer waste such as laptops, while a second will improve the navigation predictability of autonomous water vessels.
A third will dynamically adapt work and navigation paths assigned to fleets of Autonomous Mobile Robots (AMRs) when anomalies — such as low batteries or unforeseen human behavior — arise on shop floors, while the fourth will automatically assess risks in human-robot collaboration (cobot) situations to avoid accidents and reduce costs.
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