Abstract
Walking robots are well known for being able to walk over rough terrain and adapt to various environments. Hexapod robots are chosen because of their better stability and higher number of different gaits. However, having to hold the whole weight of the body and a large number of actuators makes all walking robots less energetically efficient than wheeled machines. Special methods for energy consumption optimization must be found. In this paper, hexapod robot energy consumption dependence on body elevation and step height is presented. Three main hexapod gaits are used: tripod, tetrapod and wave. Experimental results show that energy consumption does not depend on body elevation or gait. Although, higher steps increases the power consumption. Therefore, when walking over even terrain, lower step heights along with higher body elevation must be selected for tripod or tetrapod gait in order to surpass ground irregularities but still maintain maximum energetic efficiency.

Abstract
In most real multi-robot applications, such as search-and-rescue, cooperative robots have to move to complete their tasks while maintaining communication among themselves without the aid of a communication infrastructure. However, initially deploying and ensuring a mobile ad-hoc network 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. An extension of the Particle Swarm Optimization to multi-robot applications has been previously proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, thus integrating two research aspects: (i) an autonomous, realistic and fault-tolerant initial deployment strategy denoted as Extended Spiral of Theodorus (EST); and (ii) a fault-tolerant distributed search to prevent communication network splits. The exploring agents, denoted as scouts, are autonomously deployed using supporting agents, denoted as rangers. Experimental results with 15 physical scouts and 3 physical rangers show that the algorithm converges to the optimal solution faster and more accurately using the EST approach over the random deployment strategy. Also, a more fault-tolerant strategy clearly influences the time needed to converge to the final solution, but is less susceptible to robot failures.

Abstract
This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.

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.

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.