Cognitive computing and wireless communications on the edge for healthcare service robots

Intelligent robotic systems are becoming essential for industries, nuclear plants, and for harsh environments in general, such as the European Organization for Nuclear Research (CERN) particles accelerator complex and experiments. In order to increase safety and machine availability, robots can perform repetitive, unplanned, and dangerous tasks, which humans either prefer to avoid or are unable to carry out due to hazards, size constraints, or the extreme environments in which they take place. A novel robotic framework for autonomous inspections and supervised teleoperations in harsh environments is presented. The proposed framework covers all aspects of a robotic intervention, from the specification and operator training, the choice of the robot and its material in accordance with possible radiological contamination risks, to the realization of the intervention, including procedures and recovery scenarios. The robotic solution proposed in this paper is able to navigate autonomously, inspecting unknown environments in a safe way. A new real-time control system was implemented in order to guarantee a fast response to environmental changes and adaptation to different type of scenarios the robot may find in a semi-structured and hazardous environment. Components of the presented framework are: a novel bilateral master-slave control, a new robotic platform named CERNbot, and an advanced user-friendly multimodal human-robot interface, also used for the operators’ offline training, allowing technicians not expert in robot operation to perform inspection/maintenance tasks. The proposed system has been tested and validated with real robotic interventions in the CERN hazardous particle accelerator complex.

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CERNTAURO: A Modular Architecture for Robotic Inspection and Telemanipulation in Harsh and Semi-Structured Environments

Robots have been successfully used in well-structured and deterministic environments, but they are still unable to function in unstructured environments mainly because of missing reliable real-time systems that integrate perception and control. In this paper, we close the loop between perception and control for real-time obstacle avoidance by introducing a new robust perception algorithm and a new collision avoidance strategy, which combines local artificial potential fields with global elastic planning to maintain the convergence towards the goal. We evaluate our new approach in real-world experiments using a Franka Panda robot and show that it is able to robustly avoid dynamic or even partially occluded obstacles while performing position or path following tasks.

Closing the Loop: Real-Time Perception and Control for Robust Collision Avoidance with Occluded Obstacles

This chapter gives an overview of robotic disassembly for remanufacturing. The general concept, characteristics and enablers of robotic disassembly are reviewed. The chapter also outlines a systematic workflow for designing robotic disassembly cells based on techniques for assembly cell design.

Robotic Disassembly for Remanufacturing

Intelligent robotic systems are becoming fundamental actors in industrial and hazardous facilities scenarios. Aiming to increase personnel safety and machine availability, robots can help perform repetitive and dangerous tasks which humans either prefer to avoid or are unable to do because of hazards, space constraints or the extreme environments in which they take place, such as outer space or radioactive experimental areas. Teleoperated robots need user friendly and safety tools to be safely operated in harsh environments where the intervention scenarios are unstructured and most of the time dangerous for human intervention. In many robotic interventions in harsh environments, a dual arms robotic system is needed to perform difficult task such as cutting, drilling etc. To ensure the safety of the robotic system and the machines to be tele-manipulated, as well as increasing the uptime of the plants, a real-time reconfigurable self-collision avoidance system coupled to a virtual augmented reality scenario is fundamental to help the operator during the intervention. In addition, it is important to provide to the operator a uniform control system, in order to not create confusion when several operations are performed using different robotic platforms. For this reason, it is vital that the self-collision avoidance system is adaptable to the current robot hardware and software configurations. In this paper, a novel reconfigurable collision avoidance system for robot manipulation running in real time is presented. The novelty of the proposed solution is the capability to be adaptable to different robots configuration and installation taking into account different parameters like the type and the number of robotic arms, as well as their orientation. The novel system is able to avoid collision not only within the robot itself, but it can avoid collision also with external unexpected objects. The structure of the novel solution is presented, as well as its validation in the CERN accelerators facilities.

David Blanco Mulero

Papers

A Real-Time Reconfigurable Collision Avoidance System for Robot Manipulation