Domain-specific ontologies support system design and can establish a framework for fulfilling user-level, safety, or ethical requirements. The IEEE 7007-2021 Ontological Standard for ethically driven robotics and automation systems is the first industry standard to introduce a structure of ontologies concerning robot ethics and related fields, such as data privacy, transparency, responsibility, and accountability, offering a systems science approach to support the ethically aligned design of complex cyber–physical systems (CPSs) and robots particularly. This article provides a comprehensive overview of the main ontological commitments composing the foundation of the standard, the rationale behind their development, together with use cases of applications. Future directions for ethically aligned robotics and artificial intelligence (AI)-based systems along IEEE 7007-2021 are outlined, taking into account the exponentially growing fields of service and medical robotics.

For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position during its operation without being told, known as the kidnapped robot problem, complicates global localisation. In addition to that, sensor malfunction and external influences of the environment can cause unexpected errors, called outliers, that negatively affect the localisation process. This paper proposes a method based on the fusion of a particle filter with bounded-error localisation, which is able to deal with outliers in the measurement data. The application of our algorithm to solve the kidnapped robot problem using simulated data shows an improvement over conventional probabilistic filtering methods.

Many applications with mobile robots require self-localization in indoor maps. While such maps can be previously generated by SLAM strategies, there are various localization approaches that use 2D floor plans as reference input. In this paper, we present a localization strategy using floor plan as map, which is based on spatial density information computed from dense depth data of RGB-D cameras. We propose an interval-based model, called Interval Free-Space Density, that bounds the uncertainty of observations and minimizes the effects of movable objects in the environment. Our model was applied in a Monte Carlo Localization strategy and compared with traditional observation models. The results of experiments showed the robustness of the proposed method in single-camera and multi-camera experiments in home environments.

Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.

Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology's strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.

Proper place recognition on an environment that can change over time is fundamental for long-term SLAM. In such scenarios the observations obtained in the same region can drastically differ due to changes caused by semi-static objects, such as doors, furniture, etc. In this work, we extend a strategy that represents environment regions using words, based on spatial density information extracted from laser readings. This time, in order to deal with changes in the environment, our method not only builds words representing the real observations made by the robot, but also alternative multi-level words to account for possible changes in a place's observations generated by non-static objects. Place recognition is made by searching matches of sequences of N consecutive words (both real or alternatives). Experiments performed in real and simulated scenarios are shown, and demonstrate the advantages associated to the use of multi-level words.

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.

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.

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.

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.

Probabilistic approaches are extensively used to solve high-dimensionality problems in many different fields. The particle filter is a prominent approach in the field of Robotics, due to its adaptability to non-linear models with multi-modal distributions. Nonetheless, its result is strongly dependent on the quality and the number of samples required to cover the space of possible solutions. In contrast, interval analysis deals with high-dimensionality problems by reducing the space enclosing the actual solution. Notwithstanding, it cannot precise where in the resulting subspace the actual solution is. We devised a strategy that combines the best of both worlds. Our approach is illustrated by solving the global localization problem for underwater robots.

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.

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.

Ubiquitous robotics, ambient Intelligence and cloud robotics are new research topics that start to gain popularity among the robotics community. The overlap that exists now between ubirobots and AmI makes their integration together within cloud computing valuable to create a hybrid physical-digital space rich with myriad of proactive intelligent services that enhance the quality and the way of our living and working. Indeed, there is no visible cooperation or synergies between ubiquitous computing and robots communities. This paper presents a workshop that is intended to facilitate discussions and build a bridge between these communities. Moreover we hope that satellite topics such as affective computing, semantic reasoning, and humancomputer interaction will enhance such discussions and foster the research in this area. We believe that both communities can learn and benefit from their mutual experiences.

Service robotics is becoming a leading application area of human-centered technologies, and the rise of household and personal assistance robots forecasts an utopist world of human--robot collaborative society. Along the road, one of the robotics community's major tasks is to work on the harmonization of trends, standardization of terms, interfaces and technologies. It is important to keep the scientific and social progress under control through sufficient public outreach and technology dissemination. Along those lines, the IEEE Robotics & Automation Society is sponsoring a working group entitled Ontologies for Robotics and Automation that will bridge cutting edge technology to users of the services---the general public. In this paper, the background of the project is presented, the definitions and examples of descriptive systems of relations are described, showing how ontologies can help to address the above challenges.

In this article, we present the ongoing efforts within the newly formed IEEE-RAS Working Group named Ontologies for Robotics and Automation. We focus in particular on one of the four subgroups that compose this working group, called the Upper Ontology/Methodology (UpOM) subgroup. As the name indicates, the aim of this subgroup is to develop an upper ontology and a methodology for ontology building and evaluation. This methodology will be used to coordinate the distributed development of domain specific ontologies by the other three subgroups, and to generate a global ontology that we hope will contribute to the standardization process of robotics and automation. This paper presents the composition, the envisioned methodology, and the future work of the UpOM subgroup. It also discusses some general aspects related to ontologies for the robotics and automation field, and to the efforts in standardizing them.

This article discusses a newly formed IEEE-RAS working group entitled Ontologies for Robotics and Automation (ORA). The goal of this working group is to develop a standard ontology and associated methodology for knowledge representation and reasoning in robotics and automation, together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of humans, robots, and other artificial systems. In addition to describing the goal and structure of the group, this article gives some examples of how the ontology, once developed, can be used by applications such as industrial kitting.

Boundary Value Problem (BVP) Path Planners generate potential fields whose gradient descent represents navigational routes from any point of the environment to a goal position. The resulting trajectories are smooth and free of local minima. A BVP planner has been recently used to compute trajectories in known inhomogeneous environments, corresponding to different degrees of traveling difficulties. In this case, we locally distort the potential field generating regions with high or low preferences for navigating. In this paper, we extend this idea, generating an strategy allowing the robot to explore an unknown environment dynamically considering environment preferences. Several experiments demonstrate that our method can be used as a core in an integrated exploration approach.

BVP Path Planners generate potential fields whose gradient descent represents navigational routes from any point of the environment to a goal position. The resulting trajectories are smooth and free of local minima. In this paper, we proceed with our comprehensive study on possible forms for the core equation of the planner. The main goal is to allow the planner to deal with inhomogeneous environments. The navigational potential is calculated independently in different patches of the environment. Each patch has its own bias for potential concavity (or convexity), then creating regions with higher or lower traveling preferences. By using simulated experiments we compare the performance of the new BVP with some recent proposals.

BVP Path Planners generate potential fields through a differential equation whose gradient descent represents navigation routes from any point of the environment to a goal position. Resulting paths are smooth and free from local minima. In spite of these advantages, this kind of planners consumes a lot of time to produce a solution. In this paper, we present a new approach that combines our BVP Path Planner with the Full Multigrid Method, which solves elliptic partial differential equations using a hierarchical strategy. Our new approach, called Hierarchical BVP Path Planner, enables real-time performance on large environments. Results show that our proposal spends less than 1% of the time needed to compute a solution using our original planner in several environments.

This paper presents a combination of the BVP-path planner and Monte Carlo localization to assist a robot in the global localization problem in sparse environments. This kind of environment poses a very difficult situation in this problem, since several of its regions do not provide relevant information to permit the robot to recover its pose. This paper proposes a strategy that distributes particles only in relevant parts of the environment using the information about the environment structure. Afterwards, it leads the robot along these regions using the numeric solution of a BVP involving Laplace equation. In the experiments, we also show that the information about robot motion can be used to improve the convergence rate to the correct robot pose. Simulation results are presented to illustrate the potential of the method.

Abstract The animation of groups of characters involves the generation of some interesting steering behaviors like following a single path, moving toward a common objective, and moving while keeping a formation. This paper presents a new approach to manage the movement of groups in dynamic environments using a simple and robust algorithm that includes a strategy to keep formations during the displacement of the group. Our method is based on a boundary value problem (BVP) involving Laplace's equation and has two layers. In the first one, agroup mapis built to allow for local control of each individual, while in the second one a path planning is performed for each group as a whole. Results show that our technique is robust to several situations and can be implemented on GPU, which results in real‐time performance for large groups. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we introduce a modification in the Potential Field Method based on Boundary Value Problems to control multiple robots. In this new method, that we call Locally Oriented Potential Field (LOPF), the robots share a single map, but the field is adjusted locally for each robot using a bias vector. We show that the LOPF method is very appropriated to control robot soccer teams, in which each robot needs to adapt independently to the environment while aiming to perform the same task. Simulation results are presented to illustrate the richness of behaviors allowed by the technique.

We propose some techniques to improve the exploration method based on BVP and demonstrate that, at least, for exploration in two dimensional space, we can reduce enormously the computation cost of the harmonic function method for exploration without losing the important properties of the method. Switching between local and global potential calculation and using an adaptive activation window result in a computationally efficient and refined mapping process.

The use of relaxation methods for calculation of harmonic potentials has proved to be a powerful technique for path planning in a known environment and recently for exploration of unknown environments. In the latter case, the potential is calculated on partial versions of the map represented on an occupancy grid, and it indicates safe paths towards the unexplored regions. Here, we show that exploration based on potentials functions calculated from boundary value problems works fairly well in sparse environments provided that we no longer use harmonic potentials. We propose different families of potentials that improve the exploration process in this kind of environment, while keeping the important property of not having dynamical local minima.

The use of relaxation methods for calculation of harmonic potentials has proved to be a powerful technique for path planning in a known environment. We show that this idea can be successfully extended to exploration of unknown environments. The potential is calculated in a partial version of the map, represented on an occupancy grid, and it indicates safe paths towards the unexplored regions. We demonstrate that a complete relaxation of the potential is not necessary to accomplish smooth performances. Furthermore, we discuss the effect of different relaxation methods in the calculation of harmonic potential.