Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C.

The use of Cloud Computing for computation offloading in the robotics area has become a field of interest today. The aim of this work is to demonstrate the viability of cloud offloading in a low level and intensive computing task: a vision-based navigation assistance of a service mobile robot. In order to do so, a prototype, running over a ROS-based mobile robot (Erratic by Videre Design LLC) is presented. The information extracted from on-board stereo cameras will be used by a private cloud platform consisting of five bare-metal nodes with AMD Phenom 965 4 CPU, with the cloud middleware Openstack Havana. The actual task is the shared control of the robot teleoperation, that is, the smooth filtering of the teleoperated commands with the detected obstacles to prevent collisions. All the possible offloading models for this case are presented and analyzed. Several performance results using different communication technologies and offloading models are explained as well. In addition to this, a real navigation case in a domestic circuit was done. The tests demonstrate that offloading computation to the Cloud improves the performance and navigation results with respect to the case where all processing is done by the robot.Note to Practitioners-Cloud computing for robotics is very promising for several reasons, like robot's energy saving, larger storage capacity, stable electric power, better resource utilization and the difficulty of upgrading the robots' embedded hardware. The presented application extracts 3D point clouds from the stereo image pairs of a camera situated on the robot. Using these 3D points, a shared control is implemented to help the remote teleop-eration of a robot. That is, the commands sent by a joystick are attenuated when a possible collision is detected (by checking the future commanded trajectory against the 3D points). All of these computationally heavy tasks (difficult to perform by a mobile robot) are done in the cloud. The offloading models proposed in this paper are generic enough to be used in other applications. Obtained results show that further improvement in communica-tion technologies will suppose a significant performance boost for offloading computation.

Problem-based learning (PBL) has proved to be a highly successful pedagogical model in many fields, although it is not that common in computer engineering. PBL goes beyond the typical teaching methodology by promoting student interaction. This paper presents a PBL trial applied to a course in a computer engineering degree at the University of Seville, Spain. To promote the reusability and interoperability of the PBL trial, a design-driven approach was used, based on the Internet protocol Multimedia Subsystems (IMS) Learning Design (LD) standard. This paper presents the outcomes of using this method to innovate teaching practices in a blended learning environment. Design and implementation results, as well as users' opinions, are presented and analyzed.

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