Edson Prestes

An IEEE standard Ontology for Robotics and Automation

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.

Applied ontologies and standards for service robots

Service robotics is an emerging application area for human-centered technologies. The rise of household and personal assistance robots forecasts a human-robot collaborative society. One of the robotics communitys major task is to streamline development trends, work on the harmonization of taxonomies and ontologies, along with the standardization of terms, interfaces and technologies. It is important to keep the scientific progress and public understanding synchronous, through efficient outreach and education. These efforts support the collaboration among research groups, and lead to widely accepted standards, beneficial for both manufacturers and users. This article describes the necessity of developing robotics ontologies and standards focusing on the past and current research efforts. In addition, the paper proposes a roadmap for service robotics ontology development. The IEEE Robotics & Automation Society is sponsoring the working group Ontologies for Robotics and Automation. The efforts of the Working group are presented here, aiming to connect the cutting edge technology with the users of these services-the general public.

Exploratory Navigation Based on Dynamical Boundary Value Problems

The paper presents a general framework for concurrent navigation and exploration of unknown environments based on discrete potential fields that guide the robot motion. These potentials are obtained from a class of partial differential equation (PDE) problems called boundary value problems (BVP). The boundaries are generated from sensor readings and therefore they change as the robot moves. This framework corresponds to an extension of our previous work (Prestes, E., Idiart, M. A. P., Engel, P. and Trevisan, M.: Exploration technique using potential fields calculated from relaxation methods, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, p. 2012; Prestes, E., Engel, P. M., Trevisan, M. and Idiart, M. A.: Exploration method using harmonic functions, Robot. Auton. Syst.40(1) (2002), 25–42). Here, we propose that a careful choice of the PDE and the boundary conditions can produce efficient exploratory behaviors in sparse and dense environments. Furthermore, we show how to extend the exploratory behavior to produce new ones by changing dynamically the boundary function (the value of the potential at the boundaries) as the exploration takes course. Our framework is validated through a series of experiments with a real robot in office environments.

Simulating pedestrian behavior with potential fields

The main challenges of realistically simulating the displacement of humanoid pedestrians are twofold: they need to behave realistically and they should accomplish their tasks. Here we present a field potential formalism, based upon boundary value problems, that allows a group of synthetic actors to move negotiating space, avoiding collisions, attaining goals in prescribed sequences while at same time producing very individual paths. The individuality of each pedestrian can be set by changing its inner field parameters. This leads to a broad range of possible behaviors without jeopardizing its task performance. Simulate situations as behavior in corridors, collision avoidance and competition for a goal are presented and discussed.

Comparing harmonic functions and potential fields in the trajectory control of mobile robots

Potential field is a reactive method that has been used for trajectory control of mobile robots. In this method the robot behaves like a particle moving under the influence of an artificial potential produced by the target and the obstacles. This method has lower computational cost than others that utilize maps as a world model. However, one problem or this method is that it can generate regions of local minima. In these regions the driving force vanishes due to specific obstacle and goal configurations and the robot gets trapped. The use of harmonic functions for the potential calculation can solve this problem. In the harmonic function method the environment is represented by a grid, in which cells with an obstacle have their potential value set to 1 whereas cells that contain the target have their potential value set to 0. A relaxation algorithm is then employed to calculate the harmonic potential among the obstacles and the target. These two techniques have been applied in robot soccer environment to develop game strategies. Results obtained show that both techniques present better results when compared to the standard strategy or the FIRA Simulator. Further, a comparison of these techniques is presented for showing the advantages and disadvantages existing in each one of them.

Exploration technique using potential fields calculated from relaxation methods

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.

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.

Improving Monte Carlo Localization in sparse environments using structural environment information

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.

Natural steering behaviors for virtual pedestrians

The animation of humanoids in real-time applications is yet a challenge if the problem involves attaining a precise location in a virtual world (path-planning), moving realistically according to its own personality, intentions and mood (motion planning). In this paper we propose a formally complete and low-cost solution based upon boundary value problems (BVP) to control steering behaviors of characters in dynamic environments. We use a potential field formalism that allows synthetic actors to move negotiating space, avoiding collisions, and attaining goals, while producing very individual paths. The individuality of each character can be set by changing its inner field parameters leading to a broad range of possible behaviors without jeopardizing its performance. To illustrate the technique potentialities, some results exploring situations as steering behavior in corridors with collision avoidance and competition for a goal, and searching for objects in unknown environments are presented and discussed. A proposal to automatically change the size of the field of view of each agent, producing different behaviors is also a contribution of this paper. Some comments about performance are also made to help the reader to evaluate the potential of the method.

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.