Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.

In human-robot interaction situations, robot sensors collect huge amounts of data from the environment in order to characterize the situation. Some of the gathered data ought to be treated as private, such as medical data (i.e., medication guidelines), personal, and safety information (i.e., images of children, home habits, alarm codes, etc.). However, most robotic software development frameworks are not designed for securely managing this information. This paper analyzes the scenario of hardening one of the most widely used robotic middlewares, Robot Operating System (ROS). The study investigates a robot's performance when ciphering the messages interchanged between ROS nodes under the publish/subscribe paradigm. In particular, this research focuses on the nodes that manage cameras and LIDAR sensors, which are two of the most extended sensing solutions in mobile robotics, and analyzes the collateral effects on the robot's achievement under different computing capabilities and encryption algorithms (3DES, AES, and Blowfish) to robot performance. The findings present empirical evidence that simple encryption algorithms are lightweight enough to provide cyber-security even in lowpowered robots when carefully designed and implemented. Nevertheless, these techniques come with a number of serious drawbacks regarding robot autonomy and performance if they are applied randomly. To avoid these issues, we define a taxonomy that links the type of ROS message, computational units, and the encryption methods. As a result, we present a model to select the optimal options for hardening a mobile robot using ROS.

No abstract

Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning model which predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016–2017 course and later validated using results from 2017–2018 course. Results conclude that the model predicts students’ success with a success high percentage.

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.