Abstract
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization (PSO) using natural selection, or survival-of-the-fittest, to enhance the ability to escape from local optima. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots. Therefore, the RDPSO decreases the amount of required information exchange among robots, and is scalable to large populations of robots. This paper presents a stability analysis of the RDPSO to better understand the relationship between the algorithm parameters and the robot's convergence. Moreover, the analysis of the RDPSO is further extended for real robot constraints (e.g., robot dynamics, obstacles and communication constraints) and experimental assessment with physical robots. The optimal parameters are evaluated in groups of physical robots and a larger population of simulated mobile robots for different target distributions within larger scenarios. Experimental results show that robots are able to converge regardless of the RDPSO parameters within the defined attraction domain. However, a more conservative parametrization presents a significant influence on the convergence time. To further evaluate the herein proposed approach, the RDPSO is further compared with four state-of-the-art swarm robotic alternatives under simulation. It is observed that the RDPSO algorithm provably converges to the optimal solution faster and more accurately than the other approaches.

Abstract
In the end of a requirements elicitation phase, it is expectable that all information can be properly perceived by the implementation teams. In our work, we faced the problem of dealing with a large set of requirements that need to be implemented by different teams, to ensure on-time delivery. The teams are physically separated and with different working cultures and skills. The system requirements were (semi-) automatically derived from user requirements, are framed within a logical architecture (of the intended system) and the desired high-level execution scenarios are defined and included in the requirements description. Due to the large size of the architecture and to the nature of the working environment, the architecture must be divided before being delivered. In this paper, we present our approach for dividing the logical architecture of the intended system into modules to be delivered for implementation. The division in constructed upon requirement views that provide information about the modules, their interfaces, the intended execution scenarios, and the interfaces with other modules or systems. The approach is evaluated using the ISOFIN project.

Abstract
An extension of the well-known Particle Swarm Optimization (PSO) to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefited from the dynamical partitioning of the whole population of robots. Although such strategy allows decreasing the amount of required information exchange among robots, a further analysis on the communication complexity of the RDPSO needs to be carried out so as to evaluate the scalability of the algorithm. Moreover, a further study on the most adequate multi-hop routing protocol should be conducted. Therefore, this paper starts by analyzing the architecture and characteristics of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Such procedure will be the first step to achieving a more scalable implementation of RDPSO by optimizing the communication procedure between robots. Second, an ad hoc on-demand distance vector reactive routing protocol is extended based on the RDPSO concepts, so as to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots and 60 simulated robots show that the proposed methodology significantly reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

Abstract
Mobile Ad hoc Networks have attracted much attention in the last years, since they allow the coordination and cooperation between agents belonging to a multi-robot system. However, initially deploying autonomously a wireless sensor robot network in a real environment has not taken the proper attention. Moreover, maintaining the connectivity between agents in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. This paper compares two autonomous and realistic marsupial strategies for initial deployment in unknown scenarios, in the context of swarm exploration: Random and Extended Spiral of Theodorus. These are based on a hierarchical approach, in which exploring agents, named scouts, are autonomously deployed through explicit cooperation with supporting agents, denoted as rangers. Experimental results with a team of heterogeneous robots are conducted using both real and virtual robots. Results show the effectiveness of the methods, using a performance metric based on dispersion. Conclusions drawn in this work pave the way for a whole series of possible new approaches.

Abstract
This paper proposes a specific configuration of sensors assembled in a mobile robot, which can be used as a proof of concept to measure important environmental variables in an urban search and rescue mission, such as toxic gas density, temperature gradient and smoke particles density. This data is processed through a support vector machine classifier with the purpose of detecting relevant contexts in the course of the mission. The outcome is provided by the experimental experiments conducted with TraxBot and Pioneer-3DX robots under the Robot Operating System framework.

Abstract
Depression and loneliness among the elderly are one of the biggest problems affecting the world population. This leads to elderly isolation which is a major risk factor for suicide. Moreover, if isolation is coupled with physical illness and incapacitation, such a risk increases exponentially. To fight back this problem, roboticists have been proposing solutions to autonomously monitor, support and even promote physical activities among the elderly. Nevertheless, those appear as very high-cost and complex solutions that require an advanced technical expertise. Recent off-the-shelf solutions, such as the well-known NAO robot, emerged as possible alternatives. An extension to the NAO robot, denoted as RIA, is being developed at the Engineering Institute of Coimbra (ISEC). The RIA is not only built for a social interaction with the elderly but also as an autonomous tool to promote professional care through the analysis of health and environmental parameters. Therefore, the RIA robot is an adapted NAO low-cost platform equipped with several sensors that can measure different parameters like body temperature, blood pressure and heart rate. By validating this valuable platform, the foundations were laid for a whole new paradigm in elderly care. © 2013 IEEE.