Vitor A. M. Jorge

Requirements for building an ontology for autonomous robots

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.,For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.,AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.,AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.,Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

Defining positioning in a core ontology for robotics

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information.

The 2017 Humanitarian Robotics and Automation Technology Challenge [Humanitarian Technology]

Presents information on the RASS 2015 Humanitarian Robotics and Automation Technology Challenge.

Interacting with danger in an immersive environment: issues on cognitive load and risk perception

Any human-computer interface imposes a certain level of cognitive load to the user task. Analogously, the task itself also imposes different levels of cognitive load. It is common sense in 3D user interfaces research that a higher number of degrees of freedom increases the interface cognitive load. If the cognitive load is significant, it might compromise the user performance and undermine the evaluation of user skills in a virtual environment. In this paper, we propose an assessment of two immersive VR interfaces with varying degrees of freedom in two VR tasks: risk perception and basic object selection. We examine the effectiveness of both interfaces in these two different tasks. Results show that the number of degrees of freedom does not significantly affect a basic selection task, but it affects risk perception task in an unexpected way.

Integrated exploration using time-based potential rails

Integrated exploration is the most complete task in mobile robotics, and corresponds to the union of mapping, localization and motion planning. A powerful integrated exploration solution must take into account decisions that improve the quality of the map construction, such as closing loops, at the same time that the environment is explored. Potential fields and boundary value problems (BVP) have been used with success in tasks of planning, localization and exploration, but not yet in integrated strategies. In this paper, we present an integrated exploration strategy using a time varying BVP-based exploration. Our strategy consists of creating potential rails that guide the robot to regions that are either unexplored or were visited a long time ago. We also apply local distortions in the potential field to generate a loop closure strategy. Experimental results demonstrate that our method improves the quality of the map construction, keeping the balance between revisiting and exploratory activities.

Fast Monte Carlo Localization using spatial density information

Estimating the robot localization is a fundamental requirement for applications in robotics. For many years, Monte Carlo Localization (MCL) has been one of the most popular approaches to solve the global localization when using range finders, like sonars or lasers. It generally weights the estimates about the robot state by comparing raw sensor readings with simulated readings computed for each estimate. In this paper, we propose an observation model for localization that associates a kernel density estimate (KDE) to each point in the space. This single-valued density measure is independent of orientation, what allows an efficient pre-caching step, substantially boosting the computation time of the process. Using the gradient of the densities field, our strategy is able to estimate orientation information that helps to restrict the localization search space. Additionally, we can combine densities obtained by kernels of different sizes and profiles to improve the quality of the acquired information. We show through experiments in comparison with traditional approaches that our method is efficient, even working with large sets of particles, and effective.

Ouroboros: Using potential field in unexplored regions to close loops

An autonomous robot requires a map of the environment for many tasks. Yet, in many cases, this map is unavailable and the robot must build one in real-time, in the so-called integrated exploration task. Several integrated exploration approaches adopt some sort of loop-closing strategy combined with an online simultaneous localization and mapping (SLAM) technique. This is important because the robot can reduce the uncertainty about its pose by revisiting known areas. One solution for environment exploration is to use the vector field computed from the numeric solution of a Boundary Value Problem (BVP). This approach is called BVP Path Planner and generates smooth and free of local minima potential fields. However, this planner cannot actively close loops and does not scale well in large scenarios. In this paper we present a technique which performs active loop closure using the BVP Path Planner. Our proposal takes advantage of the potential of unexplored regions, and induces the robot to close loops by placing dynamic barriers at the visited space. The update of the potential field is boosted using a local window charged by a Voronoi diagram of the environment containing global information. We show through experimental results the effectiveness of the technique with a thorough discussion of its characteristics.

Using n-grams of spatial densities to construct maps

Place recognition is the frond-end of Simultaneous Localization and Mapping (SLAM). Topological representations depend on good association of vertices, which ultimately depends on the front-end. In this paper, we consider a robot lost in an unknown environment trying to construct a topological map to localize itself using a laser range finder and odometry information. The algorithm makes use of an efficient observation model based on kernel density estimates (KDEs) to detect loops. The observation model separates the map into regions denominated words, classified based on the density of free space, number of observations and segment orientation. Loop closing results from the matching of sequences of N consecutive words (n-grams). The proposed approach is orders of magnitude faster than a sequence of Iterative Closest Point (ICP) matches. The method is evaluated varying input parameters in real and simulated scenarios.

Segmented DP-SLAM

Simultaneous Localization and Mapping (SLAM) is one of the most difficult tasks in mobile robotics. While the construction of consistent and coherent local solutions is simple, the SLAM remains a critical problem as the distance travelled by the robot increases. To circumvent this limitation, many strategies divide the environment in small regions, and formulate the SLAM problem as a combination of multiple precise submaps. In this paper, we propose a new submap-based particle filter algorithm called Segmented DP-SLAM, that combines an optimized data structure to store the maps of the particles with a probabilistic map of segments, representing hypothesis of submaps topologies. We evaluate our method through experimental results obtained in simulated and real environments.

What is the effect of interface complexity on risk perception tasks

Most human computer interfaces impose a certain level of additional cognitive load to the user task. In this project we study the use of VR to evaluate the user competency for safe behavior in work environments. We propose an assessment of two interfaces with different levels of cognitive load for locomotion in a risk perception task. We describe user experiments with two similar groups of subjects, each using one of the interfaces. We identified important open questions on the design of this kind of interface.