Abstract
In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

Abstract
In robotics, a reliable simulation tool is an important design and test resource because the performance of algorithms is evaluated before being implemented in real mobile robots. The virtual environment makes it possible to conduct extensive experiments in controlled scenarios, without the dependence of a physical platform, in a faster and inexpensive way. Although, simulators should be able to represent all the relevant characteristics that are present in the real environment, like dynamic (shape, mass, surface friction, etc.), impact simulation, realistic noise, among other factors, in order to guarantee the accuracy and reliability of the results. This paper presents a ROS (Robot Operating System) framework for the SimTwo simulator. ROS is an open-source library that is commonly used for the development of robotic applications since it provides standard services and promotes large-scale integrative robotic research. SimTwo is a realistic simulation software suitable for test and design of several types of robots. This simulator conducts realistic navigation procedures, since the driving systems, the sensors, the mechanical and physical properties of the bodies are precisely modeled. The framework presented in this research provides the integration of ROS-based systems with the SimTwo simulator. Therefore, this framework reduces the risk of damage of expensive robotic platforms and it can be used for the development of new mobile robot controllers, as well as for educational purposes.

Abstract
The problem of homing a mobile robot to a given reference location under unknown relative and absolute positions is addressed in this paper. This problem is easy to solve when all the positions and kinematic variables are known or are observable, but remains a challenge when only range is measured. Its complexity further increases when variable and unknown drifts are added to the motion, which is typical for marine vehicles. Based on the range measurements, it is possible to drive the robot arbitrarily close to the reference. This paper presents a complete solution and demonstrates the validity of the approach based on the Lyapunov theory. The use of models, which are often affected by uncertainties and/or unmodeled terms, is intended to be minimal and only some constraints are imposed on the speed of the robot. We derive a control law that makes the robot converge asymptotically to the reference and prove its stability theoretically. Nevertheless, as it is well known, practical limitations on the actuation can weaken some properties of convergence, namely when the system dynamics require increasing actuation along the approach trajectory. We will demonstrate that the robot reaches a positively invariant set around the reference whose upper bound is determined. Finally, we conclude our work by presenting simulation and experimental data and by demonstrating the validity and the robustness of the method.

Abstract
A fundamental problem in the management of an automated guided vehicle system (AGVS) is the determination of the load to be transported and the vehicle to transport it. The time for the loading and unloading of pallets must be specified as soon as possible. Typical objectives are minimization of travel times and costs by the reduction of the number of vehicles required to fulfill a given transportation order. This article presents a methodology for the estimation the minimum number of AGVs (considering all the available ones at the shop floor level) required to execute a given transportation order within a specific time-window. A comparison is made between the algorithms Shortest Job First and meta-heuristic Tabu Search (applied to an initial solution) for a task assignment. An enhanced Dijkstra algorithm is used for the conflict-free routing task. The number of vehicles is estimated so as to provide an efficient distribution of tasks and reduce the operational costs of the materials handling system. Simulation results of two typical industrial warehouse shop floor scenarios are provided. Although the study focuses on pre-planning of order fulfillment of materials handling, the proposed methodology can also be utilized as an important tool for investment analysis of the warehouse layout design and for estimating the ideal number of AGVs.

Abstract
Human being has an extraordinary capability for motion perception due to its remarkable visual sensing system that makes it possible to perceive, distinguish and characterize the different moving elements of the environment. Thus, it extracts information through sensory experience and conducts reliable judgments based on intrinsic motion features, namely, location, direction, trajectory, magnitude, colors, boundary and shape. Unfortunately, the same cannot be said for mobile robots. The critical nature of visual perception for these kinds of systems turns motion detection and analysis as one of the most relevant areas discussed on the literature, existing several models and methods to perform motion analysis in a variety of environments. This paper discusses motion analysis for mobile robots. A brief description about the complexity of motion perception based on moving observations and for surveillance applications is presented. In addition, the most often encountered approaches and future orientations are also discussed.

Abstract