Abstract

Abstract
This paper addresses the problem of decentralized exploration and mapping of unknown environment by a multiple robot team. The exploration methodology relies on individual decision rules and communication of topological maps to achieve efficient and fast mapping, minimizing overlap of explored space. This distributed solution allows scalability of the proposed methods. Each robot broadcasts a graph representing the topological map, with information of exploration status of each region. Therefore, this kind of information can be transmitted to robots that are not in the communication range, through other robots in a multi-hop network. This work has been tested in simulation, and the results demonstrate the performance improvements and robustness that arise from our multirobot approach to exploration.

Abstract
This paper presents the development and the experimental validation of a centralized coordination control scheme that is robust to communication constraints and individual tracking errors for a team of possibly heterogeneous marine vehicles. By assuming the existence of a lower level target tracking control layer, a centralized potential-field-based coordination scheme is proposed to drive a team of robots along a path that does not necessarily need to be defined a priori. Furthermore, the formation is allowed to hold its position (the vehicles hold their positions with regard to a static virtual leader), which is particularly appreciated in several marine applications. As it is important to guarantee stability and mission completion in adverse environments with limited communications, the centralized control scheme for coordination is constructed in a way that makes it robust to tracking errors and intermittent communication links. The study and developments presented in this paper are complemented with field experiments in which vehicles have coordinated their operation to keep in formation over a dynamic path and static points. This work considers two types of communication technologies. Firstly, standard high rate radio communications are used to drive the formation and, secondly, acoustic communications are employed to assess the performance and the robustness of the proposed approach to degraded and highly variable conditions. Index Terms-Communication

Abstract
This research studies motion segmentation based on dense optical flow fields for mobile robotic applications. The optical flow is usually represented in the Euclidean space however, finding the most suitable motion space is a relevant problem because techniques for motion analysis have distinct performances. Factors like the processing-time and the quality of the segmentation provide a quantitative evaluation of the clustering process. Therefore, this paper defines a methodology that evaluates and compares the advantage of clustering dense flow fields using different feature spaces, for instance, Euclidean and Polar space. The methodology resorts to conventional clustering techniques, Expectation-Maximization and K-means, as baseline methods. The experiments conducted during this paper proved that the K-means clustering is suitable for analyzing dense flow fields.

Abstract
Nowadays, and considering flexibility, industrial robots still present some drawback that prevent them to be used in vast fields of the industry. One of their major limitations is related with their perception skills. In this area, and although the many developments verified on 3D object recognition systems in the research sphere, the number of solutions appearing in the industry level has been slow. Hence, this article tries to clarify some of the motives that difficult the technology transference (in what concerns object recognition) between both worlds. At the same time, it will be presented an industrial case scenario (inserted in an European Project) where some of the problems enumerated during the article are present.

Abstract
The sensors that acquire 3D data play an important role in many applications. In addition, they have been used in the robotic field for several purposes, for instance, enhancing the navigation of mobile robots, object detection, scene reconstruction, 3D inspection of parts and others. Moreover, a significant amount of devices with distinct cost, accuracy and features have been released in the recent years which increases the difficulty of comparing each sensor in a proper manner or choosing the most suitable device for a specific task and operation field. This paper compares the Kinect v1, Kinect v2, Structure Sensor and Mesa Imaging SR4000. The noise of each sensor is characterized for different distances and considering objects with different colors. Therefore, this paper proposes a simple but quantitative benchmark for evaluating 3D devices that characterizes the most relevant features for the robotic field and in accordance with different type of operations.