Abstract
This paper presents an analytical modelthat evaluates the performance of the Maximum Packing channel allocation technique on linear andplanar cellular systems. The main innovations introduced by this model are the deterministic identification of the system space-state SMP and its application to a multi-dimensional Markov chain. The model was thereafter applied to severalcellular systems with different characteristics: number of cells, number of channels, interferenceconstraints and offered traffic values. Simulation results have validated the model and shown that Maximum Packing technique provides the best performance among all the available algorithms. © Springer Science+Business Media Dordrecht 2014.

Abstract
Over time, biological societies such ashumans, ants or bees have shown us the advantages inherent to the collective work. It is based on such results that many researchers have been trying to successfully develop new approaches in Multi-Robot Systems. Nevertheless, several assumptions need to be assured for collective work toemerge. In this paper, it is presented the significance and the advantages of cooperation in thedifferent societies bridging the gap to the concept of robot society. In order to compare th advantages of cooperative robots, it is considered essential the development of computational simulation based on the robotic cooperation in unstructured environments. Hence, a Multi-Robot Simultaneous Localization and Mapping (SLAM) using Rao-Blackwellized particle filter is implemented ina simulation environment developed in the Player/Stage platform for robot and sensor applications. © Springer Science+Business Media Dordrecht 2014.

Abstract
The main aim of this research is the development of an autonomous adaptive system for actuation, data acquisition and control of activeankle-foot orthosis. In this paper the design ofa control unit composed by microcontroller, driver and sensor system, and its application to theactuation and position of the foot orthotic segment is presented. The research work combines hardware and software design of the intelligent control device with graphical interface for representation and analysis of the data acquired duringhuman motion. The dynamic system simulation is done in Matlab Simulink and SimMechanics. A laboratory model of the proposed system was implemented to demonstrate its autonomy and verify experimentally its functionality.The proposed control device can be used in several applications involving human motion analysis and control of different types of orthoses or functional electrical stimulation used for gait correction. © Springer Science+Business Media Dordrecht 2014.

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.