Abstract
Orientation estimation plays a crucial role in robotics. Precise and reliable estimation of orientation, and the yaw angle in particular, is still a challenge and subject of great concern among researchers. This paper presents the development of a platform for yaw angle estimation by fusing inertial and magnetic sensing (a low-cost multi-sensorial system composed by both a digital compass and a gyroscope). A Kalman filter is used to estimate the error produced by the gyroscope. Experimental results indicate that the proposed solution is able to eliminate the drift effect produced by gyroscope data and, at the same time, has the capacity to react to fast orientation changes.

Abstract
A multi-robot system is formed when a group of robots interact with the environment as a single system. This system can also be in formation in order to accomplish tasks rather difficult or impossible to achieve with a single robot. A nonlinear model predictive formation control (NMPFC) was used to converge a group of middle sized mobile soccer robots towards a desired target using the concept of active target tracking. This paper presents a novel approach on formation controller's weight tuning in order to minimize an objective function that reflects the controller's efficiency with respect to a given criteria. Furthermore, the results of simulation and experiment with real robots are presented and discussed.

Abstract
Purpose - The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach - In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings - Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications - The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications - Today, most of human-robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value - Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human-robot interaction scenario show the performance of the system.

Abstract
A nonlinear model predictive formation controller (NMPFC) was used to converge a group of middle sized mobile soccer robots towards a desired target using the concept of active target tracking. This paper presents a novel approach on the formation controller's weight tuning in order to minimize an objective function that reflects the controller's efficiency with respect to a given criteria. This method is here called Iterative Weight Tuning (IWT). In this paper the effectiveness from the proposed method is shown by the results of simulations and experiment with real robots, compared to the tuning performed using genetic algorithms approach. The results demonstrated that the IWT method was successful in achieving a better set of weights that influenced the formation controller to converge the robots into formation in a better fashion regarding the agents' objective function.

Abstract

Abstract
This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of target's absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed.