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SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COB's existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper

Traumatic Brain Injury (TBI) is recognized as an important cause of death and disabilities after an accident. The availability a tool for the early diagnosis of brain dysfunctions could greatly improve the quality of life of people affected by TBI and even prevent deaths. The contribution of the paper is a process including several methods for the automatic processing of electroencephalography (EEG) data, in order to provide a fast and reliable diagnosis of TBI. Integrated in a portable decision support system called EmerEEG, the TBI diagnosis is obtained using discriminant analysis based on quantitative EEG (qEEG) features extracted from data recordings after the automatic removal of artifacts. The proposed algorithm computes the TBI diagnosis on the basis of a model extracted from clinically-labelled EEG records. The system evaluations have confirmed the speed and reliability of the processing algorithms as well as the system's ability to deliver accurate diagnosis. The developed algorithms have achieved 79.1% accuracy in removing artifacts, and 87.85% accuracy in TBI diagnosis. Therefore, the developed system enables a short response time in emergency situations and provides a tool the emergency services could base their decision upon, thus preventing possibly miss-diagnosed injuries.

To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach.

The concept of service robotics has grown considerably over the past two decades with many robots being used in non-industrial environments such homes, hospitals and airports. Many of these environments were never designed to have mobile service robots deployed within them. This paper describes some of the challenges that are faced and need to be overcome in order for robots to successfully work in nonindustrial environments (specifically homes and hospitals). These include the problems caused by an environment not having been designed to be robot-friendly, the unstructured nature of the environment and finally the challenges presented by certain user populations who may have difficulties interacting with a robot.