The task of grasping objects is a simple and routinely action for humans but it is complex for robots. To integrate robots into everyday tasks, they have to be equipped with capabilities human-like dexterity. In this line, we propose an analytic method, called GeoGraspEvo, to compute grasping points to be used by robotic hands with three, four or more fingers. Our proposal uses features computed from visible surface objects captured by a single RGBD image of a scene. Additionally, it uses as input some configurable kinematic parameters to be able to carry out the grasping depending on the hand morphology. The method compute grasping points with no training process.

LiDAR sensors are used for mapping tasks, LiDAR odometry or 3D environment reconstruction. Several of them count with a high number of vertical layers, which increase their price and prevents research groups from carrying out experiments and scientific advances. In this paper, we propose a method for augmenting point cloud data by bilinear interpolation in a Spherical Range Image. Our method improves others on the state-of-the-art by means of standard deviation filtering of the newly generated layers. The system operates at a frequency greater than 10 Hz for data interpolation up to 20 times. In addition, we present two applications for our approach such as LiDAR odometry and LiDAR-Camera fusion, obtaining better results than others that do not apply data augmentation. Finally we make available to the scientific community a package development on ROS (Robot Operating System). The code is available at https://github.com/EPVelasco/lidar-camera-fusion

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of “valley” areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road’s shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system’s robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road’s center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.

Nowadays, robotics education programs are used to promote the skills of students in STEM content at different levels of education for a wide variety of studies, not just engineering. This paper describes an educational program of robotics, applied precisely to the laboratory sessions of a robotics initiation course taught in the first year of a degree in Robotic Engineering, as well as the results of the application of that program in different academic years. The educational program contemplates that the students, grouped in work teams, have to design, assemble, and program a small mobile robot, destined to a final competition in which all the teams participate.

Nowadays, Robotics is increasing its importance for the marine environment in our society. It allows to perform surveillance and data sampling tasks reducing the current cost of these tasks. This paper presents the design of a prototype of an Autonomous Surface Vessel (ASV) based on catamaran shape. It uses the screw theory for the propulsion system, which provides high manoeuvrability to the vessel. Furthermore, the parts of the ASV are designed to be printed by a RepRap 3D printer. This gives flexibility to check the performance of the vessel. Also the software control scheme of the vessel is presented.

This paper presents a novel control approach to dynamic visual servo control multi-fingered robotic hands for the manipulation performance of an object. The control approach allows the path tracking of the object motion by means of visual information and takes into account both the dynamics model of the robot hand and the grasping force of the fingertips. In addition, an optimal approach is employed to obtain the desired behaviour in the joint space based on an indicated cost function which determines how the control effort is distributed over the robot fingers' joints. Finally, authors show experimental verifications on a robotic manipulation system for some of the visual servo controllers derived from the control approach.

This paper analyzes the learning experiences and opinions obtained from a group of undergraduate students in their interaction with several on-line multimedia resources included in a free on-line course about Computer Networks. These new educational resources employed are based on the Web2.0 approach such as blogs, videos and virtual labs which have been added in a web-site for distance self-learning.

The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for intelligent human-robot collaboration. This paper evaluates an inertial motion capture system which registers full-body movements of an user in a robotic manipulator workplace. However, the presence of errors in the global translational measurements returned by this system has led to the need of using another localization system, based on Ultra-WideBand (UWB) technology. A Kalman filter fusion algorithm which combines the measurements of these systems is developed. This algorithm unifies the advantages of both technologies: high data rates from the motion capture system and global translational precision from the UWB localization system. The developed hybrid system not only tracks the movements of all limbs of the user as previous motion capture systems, but is also able to position precisely the user in the environment.

This paper shows the state of art teleoperation and simulation systems and proposes some possible network architectures devoted to the development of such systems. A method for the optimization of robot tasks is also suggested. The application proposed can teleoperate a robot arm with five degrees of freedom through Internet by using a previous simulation system and visual feedback. This simulation can be accessed simultaneously by different workers. As a result, the number of robots required can be reduced on new applications or dangerous environments.