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This paper presents a control architecture for an autonomous underwater vehicle (AUV) named the Component Oriented Layer-based Architecture for Autonomy (COLA2). The proposal implements a component-oriented layer-based control architecture structured in three layers: the reactive layer, the execution layer, and the mission layer. Concerning the reactive layer, to improve the vehicle primitives' adaptability to unknown changing environments, reinforcement learning (RL) techniques have been programmed. Starting from a learned-in-simulation policy, the RL-based primitive cableTracking has been trained to follow an underwater cable in a real experiment inside a water tank using the Ictineu AUV. The execution layer implements a discrete event system (DES) based on Petri nets (PNs). PNs have been used to safely model the primitives' execution flow by means of Petri net building block (PNBBs) that have been designed according to some reachability properties showing that it is possible to compose them preserving these qualities. The mission layer describes the mission phases using a high-level mission control language (MCL), which is automatically compiled into a PN. The MCL presents agreeable properties of simplicity and structured programming. MCL can be used to describe offline imperative missions or to describe planning operators, in charge of solving a particular phase of a mission. If planning operators are defined, an onboard planner will be able to sequence them to achieve the proposed goals. The whole architecture has been validated in a cable tracking mission divided in two main phases. First, the cableTracking primitive of the reactive layer has been trained to follow a cable in a water tank with the Ictineu AUV, one of the research platforms available in the Computer Vision and Robotics Group (VICOROB), University of Girona, Girona, Spain. Second, the whole architecture has been proved in a realistic simulation of a whole cable tracking mission.

Clinical Decision Support Systems (CDSSs) should form an important part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, including knowledge maintenance and continuous learning, from diagnosis and investigation through surgery, treatment and long-term care. The work presented shows a workflow-based CDSS designed to give case-specific assessment to clinicians during complex surgery or Minimally Invasive Surgerys (MISs). Following a perioperative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to provide support at any particular point of the process. The graphical user interface allows easy navigation through the whole support progress, from the initial configuration steps to the final results organized as sets of experiments easily visualized in a user-friendly way. The eXiTCDSS tool is presented giving support to a recent complex minimally invasive surgery

Abstract-This paper proposes a field application of a highlevel Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a Direct Policy Search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICT IN EU AU V .

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