Intelligent Autonomous Robots and Systems Research Line coordinates the R&D activity related to autonomous systems and intelligent automation technologies, their application in specific vertical markets, and their relation to other technologies.
The challenges addressed, and current technological trends are:
- Adaptive Systems – Biologically-inspired and bottom-up approach to AI and the kind of intelligent behavior emerging from the interaction of an embodied agent within a dynamic uncertain complex environment.
- Autonomous Systems – Decisional autonomy, situational awareness and action selection in cyber-physical systems spanning from wheeled robots to drones, taking behavior-based robotics (reactive planning) approach.
- Cognitive Robotics –Inspired on Cognitive neuroscience for bringing more engineered and symbolic (top-down) approaches to AI and address with guarantees, challenges such as human-robot interaction and social robotics.
- Development and Epigenetic Robotics –Exploit and combine concepts and methods such as self-organization, collective intelligence, deep learning and reinforcement learning, for agents which acquire (implicit) knowledge from its experience in continuous interaction with the environment.
- Robotic Systems Development –. Combination of traditional software and systems engineering concepts, methods and tools with new AI systems development ones, especially on MBSE (Model-Based Software Engineering), for successful design, construction, verification and validation of real-world autonomous cyber-physical systems.
- Robot and Robotic System Safety - Robot ethics, safety by design and human-robot interaction safety; specifically, on affective mechanisms to embed safety guarantees in intelligent autonomous systems for operation in proximity to and/or in collaboration with humans.
AI-based Cyber-Physical Systems (CPS). Intelligent Robotics Systems, or Technical Systems of Networked Computers, Robots, and AI, interact with the physical world to attain a goal, connected to their environment through the IoT, and getting unlimited computational, data and networking resources from the cloud. Assuring the autonomy of those systems (correctness, robustness, reliability, safety, etc.), which are designed to continuously adapt (e.g. by learning) to its environment, possesses important challenges to developers and roboticists. The Research Line is working in this field through the following project:
- E-FLY: Builds a distributed UAV, Edge and Cloud AI image processing framework for the analysis of infrastructures. Responsibility: collision warning system and collision avoidance assistance to UAV operators.
Cloud Robotics and Decentralized AI. AI-based CPS designers and vendors are under pressure to provide increasingly intelligent behavior at competitive costs. Increasing on-board computation of robots raises their costs and energy demand while reducing autonomy. Cloud Robotics’ aim is to bring cloud technologies in terms of processing power, storage and services to extend robots capabilities. Even if powerful, drawbacks recognized for Cloud robotics approach relate to the lack of adequacy for tasks requiring real-time processing, low latency and the requirement for continuous internet connectivity. AI decentralization strategies, such as edge computing, have emerged to address those drawbacks.
- DECENTER: The goal of the project is to deliver a robust Fog Computing Platform, covering the whole cloud-to-things continuum, that will provide orchestration and provisioning of federated resources for AI (Artificial Intelligence) applications and systems. Responsibility: connected robots and deployment of robotic tasks at the edge (edge robotics).
Robot Safety. Designing CPS for operation in proximity to and/or in collaboration with humans means that current safety engineering and legal mechanisms need to be revisited to ensure that individuals –and their properties– are not harmed. AI-based CPS need to go beyond being fail-safe to become safe-operational, that is, to maintain their behavior within safety boundaries even during the time it takes to reach a safe state after a failure. Atos is working with the embodied cognition and affective computing communities in AI to embed trust and safety mechanisms in autonomous robots to ensure robots remain safe-operational in dynamic, complex and unpredictable environments.
IAR&S Research Line contributes to shaping the future Atos Industry 4.0 and IoT offering. It envisions intelligent autonomous systems and robots that streamline real-world operations in a wide range of industries by means of real-time automated data-driven decisions and actions.
In addition, AI technologies enable a digital future where products and services deliver people-first experiences to customers. AI adds an adaptive layer on top of existing IT and addresses the complexity arising from the interplay between Big Data, IoT and Cyber-Physical Systems (such as autonomous cars).