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.

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.

Rough terrain, such as the rubble that one would expect to find in urban disaster areas, poses a severe impediment to robot mobility. The goal of this paper' is to develop methods for quantifying the difficulty a robot would encounter traversing such a region of rough terrain. Towards this, three traversability metrics describing rough terrain robot mobility are developed. In order to simplify the problem, we assume that the rough terrain in question can be discretized in a certain manner and then develop the metrics for this discretized version of the terrain. Two of the developed metrics reflect the difficulty a robot would have trying to move over the entire region of terrain (referred to as coverability). The third metric, referred to as crossability, describes the difficulty a robot would encounter attempting to move from some fixed point on the terrain to some other fixed point. We verify the applicability of the developed metrics using NIST-developed repeatable test methods (step fields) and discuss the validity of the obtained results.

As unmanned ground vehicles take on more and more intelligent tasks, determination of potential obstacles and accurate estimation of their position become critical for successful navigation and path planning. The performance analysis of obstacle mapping and unmanned vehicle positioning in outdoor environments is the subject of this paper. Recently, the National Institute of Standards and Technology's (NIST) Intelligent Systems Division has been a part of the Defense Advanced Research Project Agency LAGR (Learning Applied to Ground Robots) Program. NIST's objective for the LAGR Project is to insert learning algorithms into the modules that make up the NIST 4D/RCS (Four Dimensional/Real-Time Control System) standard reference model architecture which has been successfully applied to many intelligent systems. We detail world modeling techniques used in the 4D/RCS architecture and then analyze the high precision maps generated by the vehicle world modeling algorithms as compared to ground truth obtained from an independent differential GPS system operable throughout most of the NIST campus. This work has implications, not only for outdoor vehicles but also, for indoor automated guided vehicles where future systems will have more and more onboard intelligence requiring non-contact sensors to provide accurate vehicle and object positioning

In this paper, we describe our recent efforts on symbolic representations enabling autonomous driving in dynamic environments such as on-road driving. In contrast to traditional bottom-up approaches where temporal accumulation of locally insignificant inaccuracies causes eventual failures in high-level scene interpretation, our proposed knowledge-driven top-down approach combined with vehicle's intentions can provide valuable information to guide low-level bottom-up tasks and vice-versa. We contend that such rich symbolic representations can reduce the burden on sensory processing thereby dynamically directing it to look for particular features in expected locations and subsequently facilitating the vehicle to better react to potentially dangerous situations, such as the appearance of pedestrians in the road. We demonstrate the proposed approaches in various scenarios pertaining to vehicle perception and control using field data obtained from a military unmanned ground vehicle (UGV) traversing urban environments

We have developed PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE incorporates a long-term (LT) prediction approach based on situation recognition and a short-term (ST) prediction approach based on vehicle models. These two approaches provide for the prediction of the future location of moving objects at various levels of resolution at the frequency and level of abstraction necessary for planners at different levels within the hierarchy using sensory data. In this paper, we demonstrate the ability to use the results of the short-term prediction algorithms to strengthen/weaken the estimates of the long-term prediction algorithms via experimental results. We provide experimental results in an autonomous on-road driving scenario using AutoSim, a high-fidelity simulation tool that models details about road networks, including individual lanes, lane markings, intersections, legal intersection traversability, etc

In this paper, we present PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (real-time control system) architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. The primary contribution of this paper is the approach that is used to provide a more synergistic framework, in which the results from one prediction algorithm are used to strengthen/weaken the results of another prediction algorithm. In particular, we describe how the results of an estimation-theoretic short-term (ST) prediction algorithm are used to validate the results of a situation-based long-term (LT) prediction algorithm. We show the results of the long-term prediction both before and after the results of the short-term predictions are integrated, and demonstrate how the latter provide better predictions of vehicle future positions using the AutoSim simulation package

A coordinated control scheme is developed for a team of autonomous Unmanned Ground Vehicles (UGVs) engaged in outdoor terrain mapping. It is based on a systems- and control-oriented enhancement of the "intelligent agent" paradigm which we call the Hybrid Intelligent Control Agent (HICA). HICA essentially embeds a hybrid control system in an intelligent agent. It is used as a conceptual basis for the synthesis of coordinated controllers for certain classes of distributed multi-mode dynamical processes. In the formulation, each vehicle is modelled as a HICA with its own embedded hybrid control primitives and planning/coordination logic. The scheme is demonstrated experimentally.

An iterative temporal registration algorithm is presented in this paper for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both the unavailability and unreliability of Global Positioning System (GPS) within required accuracy bounds for unmanned ground vehicle (UGV) navigation. After suitable modifications to the well-known iterative closest point (ICP) algorithm, the modified algorithm is shown to be robust to outliers and false matches during the registration of successive range images obtained from a scanning LADAR rangefinder on the UGV. Towards registering LADAR images from the UGV with those from an unmanned aerial vehicle (UAV) that flies over the terrain being traversed, we then propose a hybrid registration approach. In this approach to air to ground registration to estimate and update the position of the UGV, we register range data from two LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. Registration of range data guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Temporal range registration enables position information to be continually maintained even when both vehicles can no longer maintain GPS contact. We present results of the registration algorithm in rugged terrain and urban environments using real field data acquired from two different LADARs on the UGV.

In this paper, we present a hierarchical multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.

Sensor-centric navigation of unmanned ground vehicles (UGVs) operating in rugged and expansive terrains requires the competency to evaluate the utility of sensor information such that it results in intelligent behavior of the vehicles. In this paper, we propose an entropic information metric for the above purpose where entropy is used to quantify the probabilistic uncertainty in sensor measurements. We present results using data obtained from field trials on an unmanned vehicle to substantiate the utility of the proposed metric. We also show how low and high level tasks can be predicated upon this metric in potential application areas related to autonomous vehicle navigation.

For an autonomous vehicle to drive in a dynamic environment, it must be able to detect moving objects, predict their future location, represent this information in its internal knowledge bases, and make appropriate plans based upon all of this information. The estimation of what the object is and where it is expected to be in the future must be continuously refined as time progresses. This paper discusses a multi-representational approach to capturing important characteristics about moving objects in a dynamic environment. Specifically we look at applying a combination of symbolic, equation-based, and grid-based representations to fully represent the information that is needed for different types of planners to develop appropriate plans in the presence of moving objects.

We describe a scheme for building terrain maps of realistic outdoor environments by having multiple robotic agents navigate in them. The terrain map combines vision-based depth information of environmental features with an elevation gradient created by fusing vertical displacements obtained from inclinometer pitch angles with DGPS altitude data. Experimental results are presented to illustrate the practical application of this scheme.

The paper describes a terrain-aided navigation system that employs points of maximum curvature extracted from laser scan data as primary landmarks. A scale space method is used to extract points of maximum curvature from laser range scans of unmodified outdoor environments. This information is then fused with odometric information to provide localization information for an outdoor vehicle. The method described is invariant to the size and orientation of the range images under consideration (with respect to rotation and translation), is robust to noise, and can reliably detect and localize naturally occurring landmarks in the operating environment. The algorithm is demonstrated in the application of a road vehicle in an unmodified operating domain.

An extended Kalman filter (EKF)-based algorithm for the localization of a team of robots is described in this paper. The distributed EKF localization scheme is straightforward in that the individual robots maintain a pose estimate using EKFs that are local to every robot. We then show how these results can be extended to perform heterogeneous cooperative localization in the absence or degradation of absolute sensors aboard the team members. The proposed algorithms are implemented using field data obtained from a team of ATRV-mini robots traversing on uneven outdoor terrain.

This paper proposes a numerically efficient tracking controller for polynomic nonlinear systems by a combined Volterra series and an extended Kalman filtering approach. The developed state estimation approach is applied for the state estimation of the states of a pendulum model, and the tracking control of the pendulum is accomplished by utilizing the internal model principle and the feedback linearization method. It is demonstrated that this approach is more numerically efficient as compared to the standard extended Kalman filtering approach and thus also leads to a numerically efficient control scheme.

This paper describes a series of trials that were done at an underground mine in New South Wales, Australia. Experimental results are presented from the data obtained during the field trials and suitable sensor suites for an autonomous mining vehicle navigation system are evaluated.

This paper reports on the map-building and map-based localization of a load-haul-dump (LHD) truck in an underground mine using statistical pattern-matching techniques utilizing range images obtained from a scanning laser range-finder The map-building approach is based on an extended Kalman filter (EKF) and the resulting map is composed of poly-lines. Three approaches are proposed for the localization of the vehicle, namely the iterative closest point (ICP) approach, a reflective beacon based approach and the combined ICP-EKF approach, wherein, the last two approaches explicitly take into account the uncertainty associated with the observation data. These approaches are then compared using data gathered from an underground mine in Queensland, Australia for their relative merits subject to various factors and the corresponding results are presented.

A new approach for the autonomous navigation of a load-haul-dump (LHD) truck in an underground mine is presented. The development of a minimal-structure combined ICP-EKF algorithm utilizing a scanning-laser range-finder for the localization of the vehicle is described. The iterative closest point (ICP) algorithm is employed for matching the scanned data to an existing map in the form of a poly-line. This combined approach efficiently deals with the uncertainty present in the range data. An extended Kalman filter (EKF) algorithm is employed, that exploits a nonlinear kinematic model incorporating the vehicle-slip, a nonlinear observation model based on the vertices of the poly-line map, and the bearing of the laser-observations. This provides reliable vehicle estimates. Real data gathered during a trial run in the mine is employed in testing the efficiency of this approach which is found to be robust with respect to occlusions and outliers, demonstrating the successful navigation of the LHD.