Abstract
Ensuring a mobile ad-hoc network (MANET) in real and complex environments is an arduous task since the strength of the connection between two robots can rapidly change over time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications was recently proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, by integrating a fault-tolerant distributed search in order to prevent communication network splits. To that end, each robot performs packet forwarding according to a paradigm of multi-hop communication, thus maintaining a maximum range or minimum signal quality between its "best" neighbors. This results in a sum of virtual forces for each robot to ensure a multi-connected MANET over time. Experimental results with 15 physical robots show that a more fault-tolerant strategy clearly influences the time needed to converge to the final solution but is less susceptible to robot failures.

Abstract
The Robotic Darwinian Particle Swarm Optimization (RDPSO) recently introduced in the literature has the ability to dynamically partition the whole population of robots based on simple "punish-reward" rules. Although this evolutionary algorithm enables the reduction of 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 its scalability. This paper analyses the architecture of the RDPSO communication system, thus describing the dynamics of the communication data packet structure shared between teammates. Moreover, a set of simple communication rules is also proposed in order to reduce the communication overhead within swarms of robots. Experimental results with teams of 15 real robots show that the proposed methodology reduces the communication overhead, thus improving the scalability and applicability of the RDPSO algorithm.

Abstract

Abstract
This paper presents a statistical significance analysis of a modified version of the Particle Swarm Optimization (PSO) on groups of simulated robots performing a distributed exploration task, denoted as RDPSO (Robotic DPSO). This work aims to evaluate this novel exploration strategy studying the performance of the algorithm under communication constraints while increasing the population of robots. Experimental results show that there is no linear relationship between the number of robots and the maximum communication range. In general, the decreased performance by the developed algorithm under communication constraints can be overcome by slightly increasing the number of robots as the maximum communication range is decreased. © 2013, Springer Science+Business Media Dordrecht.

Abstract
This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively denoted as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biologically inspired techniques to the multi-robot systems domain, by considering obstacle avoidance and communication constraints. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a 'punish-reward' mechanism, thus enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment shows the superiority of the RDPSO evidencing that sociobiological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue). Moreover, the performance of the RDPSO is further evaluated within a population of up to 12 physical robots under communication constraints. Experimental results with real platforms show that only 4 robots are needed to accomplish the herein proposed mission and, independently on the number of robots and maximum communication distance, the global optimum is achieved in approximately 90% of the experiments.

Abstract
Portugal stands out as a recognized country in the production of superior quality wine, the two main reasons being the edaphoclimatic conditions and the grapevine heritage. To maximize quality it is important that the various steps of the winemaking process be submitted to effective control and monitoring. Since the alcoholic fermentation is a crucial stage of the winemaking process, this paper describes a low cost prototype to perform the supervision and control of the alcoholic fermentation process in a winery. To demonstrate the viability of the practical application of this solution in real conditions, a prototype was installed in the winery of Escola Superior Agraria de Coimbra (ESAC), to control the fermentation temperature of white must in a medium size vat.