Project description
Reliable and safe autonomous robotic software adaptation
Future robots must possess the capability to adapt to changes, including interactions with humans. As adaptations cannot compromise reliability, robots must change behaviour while maintaining or improving performance and safety. The EU-funded RoboSAPIENS project will develop safe, trustworthy and efficient autonomous robotic software adaptations and establish a solid foundation to ensure these software adaptations are deployed safely, reliably and efficiently. This will allow for open-ended self-adaptation while incorporating safety by design. Based on these foundations, the project aims to create ‘first-time-right’ design tools and robotic platforms. The project will showcase the trustworthy robotic self-adaptation on an industrial disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel and human–robotic interaction.
Objective
The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans.
Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery
into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining
or even increasing its expected performance, and stays at least as safe and robust as before.
RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software
adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation
with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms,
and will validate and demonstrate them up to TRL4.
To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives.
1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes.
2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation.
3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation.
4. It will assure trustworthiness of systems that use both deep-learning and computational architectures for robotic self-adaptation.
To realise these objectives, RoboSAPIENS will extend techniques such as MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) and
Deep Learning to set up generic adaptation procedures and also use an SSH dimension.
RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial
disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.
Fields of science
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robots
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
8000 Aarhus C
Denmark