Abstract

Abstract
ROS (Robot Operating System) is a framework for the development of robotic applications widely used throughout research community due to its modular architecture and distributed nature. Using this framework a robot application consists of several nodes that exchange data over a common middle-ware. Programming new nodes is done by using a ROS API (application programming interface) on one of the available programming languages, such as C++ and python. It is our intention to build a robot that needs to be partially programmed in IEC 61131-3, allowing the end-user to adapt it to any specific industrial environment. In this work we have specified a mapping between the concepts defined in IEC 61131-3 and ROS, and started implementing a library through which IEC 61131-3 programs may co-ordinate their actions with the remaining ROS based robotic application. © 2013 IEEE.

Abstract
This paper describes the implementation of a navigation algorithm for Autonomous Surface Vehicles (ASVs), that is composed by two stages: 1) spline curve follower and; 2) reactive collision avoidance, obeying to the International Regulations for Preventing Collisions at Sea (COLREGs). The spline curve follower determines path's parametric functions that the vehicle should follow, taking into account : 1) the initial and goal points on the fixed world frame and; 2) the final desired orientation for the ASV. The reactive collision avoidance substitutes the splines navigation in situations of potential collision with moving obstacles. To do this, the algorithm considers the relative velocity between the controlled ASV and the moving obstacle (other ASV). It also takes into account the escape trajectory that the controlled ASV is capable to perform at each instant. The algorithm was implemented under the Robotic Operating System (ROS) framework. An intuitive spline curve configuration tool, using the RVIZ's package. The paper presents results of the simulation of two ASVs, following predefined spline trajectories, and the reactive collision avoidance routine in a rendezvous situation. A reference for a video illustrating the navigation algorithm is also provided.

Abstract
Develop ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge. Because of two main reasons: harsh condition of the terrain and unstable localization accuracy got from Global Positioning Systems (GPS). This paper presents a hybrid SLAM (VineSLAM) considering low cost landmarks to increase the robot localization accuracy, robustness and redundancy on these steep slope vineyards. Also, we present a cost-effective robot to carry-out crop monitoring tasks in steep slope vineyard environment. Test results got in a simulated and in a real test case supports the proposed approach and robot.

Abstract
In this article, a course that explores the potential of learning ROS using a collaborative game world is presented. The competitive mindset and its origins are explored, and an analysis of a collaborative game is presented in detail, showing how some key design features lead participants to overcome the challenges proposed through cooperation and collaboration. The data analysis is supported through observation of two different game simulations: the first, where all competitors were playing solo, and the second, where the players were divided in groups of three. Lastly, the authors reflect on the potentials that this course provides as a tool for learning ROS.

Abstract
Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1-2 cm in translation error and 1 degrees-3 degrees degrees in rotation error while requiring only 5-35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise.