Abstract
Inertial measurement units (IMU) are, typically, a cluster of accelerometers, gyroscopes and magnetometers. Its use was introduced with military applications, being, nowadays, widely common on industrial applications, namely robot navigation. Since there are a lot of units in different cost ranges, it is proposed, in this paper, a procedure to compare their performance in tracking tasks. Once IMU samples are unavoidably corrupted by systematic and stochastic errors, a calibration procedure (without any external equipment) to identify sensors’ error models and a Kalman filter implementation to remove white noise are suggested. Then, the comparison is carried out over two trajectories, square and circular paths, respectively, being described by a robotic arm, which acts as reference. The results show that different manufacturing quality units can track, with success, orientation references but are incapable to perform position tracking activities. © 2019, Springer Nature Switzerland AG.

Abstract
This paper addresses the problem of collision avoidance along specified paths in multiple mobile robot systems. These collisions can be represented by points of intersection or coincident segments between paths. The proposal of the work is to model these segments where the collision is possible through fictitious points. In addition, the advantages of the nonlinear versus mixed integer linear formulation, widely used in the literature, are verified. Comparisons were made and it’s proved the superiority of the proposed method with respect to complexity, computational time and inclusion of nonlinear constraints. Moreover, the simulations performed using this technique indicate that the method is promissory for applications in real systems. © Springer International Publishing AG 2018.

Abstract
The localization algorithms have different errors which can impair the robot's navigation. In this way, we propose an approach that will supervise the localization while the robot navigate. Our approach is based on another work present in the literature, where we detected a problem during its analysis. Therefore, this article will present a new method based on the RLS algorithm, to solve the identified problem. Besides, we propose the supervision of two more localization algorithms, being now four the supervised algorithms, namely: Augmented Monte Carlo Localization, Extended Kalman Filter with Beacons, Perfect Match and Odometry. The results show that the robustness and reliability of the system were increased.

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