Competitions provide a technique for building interest and collaboration in targeted research areas. This paper will
present a new competition that aims to increase collaboration amongst Universities, automation end-users, and
automation manufacturers through a virtual competition. The virtual nature of the competition allows for reduced
infrastructure requirements while maintaining realism in both the robotic equipment deployed and the scenarios. Details
of the virtual environment as well as the competitions objectives, rules, and scoring metrics will be presented.
Robot navigation in complex, dynamic and unstructured environments demands robust mapping and localization
solutions. One of the most popular methods in recent years has been the use of scan-matching schemes where
temporally correlated sensor data sets are registered for obtaining a Simultaneous Localization and Mapping
(SLAM) navigation solution. The primary bottleneck of such scan-matching schemes is correspondence determination,
i.e. associating a feature (structure) in one dataset to its counterpart in the other. Outliers, occlusions,
and sensor noise complicate the determination of reliable correspondences. This paper describes testing scenarios
being developed at NIST to analyze the performance of scan-matching algorithms. This analysis is critical for the
development of practical SLAM algorithms in various application domains where sensor payload, wheel slippage,
and power constraints impose severe restrictions. We will present results using a high-fidelity simulation testbed,
the Unified System for Automation and Robot Simulation (USARSim).
PRIDE (PRediction In Dynamic Environments) is a hierarchical
multi-resolutional framework for moving object
prediction. PRIDE incorporates multiple prediction algorithms into a single, unifying framework. To date, we
have applied this framework to predict the future location of autonomous vehicles during on-road driving. In
this paper, we describe two different approaches to compute long-term predictions (on the order of seconds into
the future) within PRIDE. The first is a cost-based approach that uses a discretized set of vehicle motions and
costs associated with states and actions to compute probabilities of vehicle motion. The cost-based approach
is the first prediction approach we have been using within PRIDE. The second is a fuzzy-logic-based approach
that deals with the pervasive presence of uncertainty in the environment to negotiate complex traffic situations.
Using the high-fidelity physics-based framework for the Unified System for Automation and Robot Simulation
(USARSim), we will compare the performance of the two approaches in different driving situations at
traffic intersections. Consequently, we will show how the two approaches complement each other and how their
combination performs better than the cost-based approach only.
This paper presents the motivation behind the new joint NIST/IEEE Virtual Manufacturing Automation Competition (VMAC). This competition strives to take the Automated Guided Vehicle (AGV) user community driven requirements and turn them into a low-entry-barrier competition. The objectives, scoring, performance metrics, and operation of the competition are explained. In addition, the entry-barrier lowering infrastructure that is provided to competitors is presented.
KEYWORDS: Roads, Sensors, Detection and tracking algorithms, Intelligence systems, Databases, Data modeling, Process modeling, Video, Control systems, Vehicle control
4D/RCS is a hierarchical architecture designed for the control of intelligent systems. One of the main areas
that 4D/RCS has been applied to recently is the control of autonomous vehicles. To accomplish this, a hierarchical
decomposition of on-road driving activities has been performed which has resulted in implementation
of 4D/RCS tailored towards this application. This implementation has seven layers and ranges from a journey
manager which determines the order of the places you wish to drive to, through a destination manager which
provides turn-by-turn directions on how to get to a destination, through a route segment, drive behavior, elemental
maneuver, goal path trajectory, and then finally to servo controllers.
In this paper, we show, within the 4D/RCS architecture, how knowledge-driven top-down symbolic representations
combined with low-level bottom-up tasks can synergistically provide valuable information for on-road
driving better than what is possible with either of them alone. We demonstrate these ideas using field data
obtained from an Unmanned Ground Vehicle (UGV) traversing urban on-road environments.
PRIDE is a hierarchical multiresolutional 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) reference model architecture and provides information to planners at the level of granularity that
is appropriate for their planning horizon. This framework supports the prediction of the future location of
moving objects at various levels of resolution, thus providing prediction information at the frequency and level of
abstraction necessary for planners at different levels within the hierarchy. To date, two prediction approaches have
been applied to this framework. In this paper, we provide an overview of the PRIDE (Prediction in Dynamic
Environments) framework and describe the approach that has been used to model different aggressivities of
drivers. We then explore different aggressivity models to determine their impact on the location predictions
that are provided through the PRIDE framework. We also describe recent efforts to implement PRIDE in
USARSim, which provides high-fidelity simulation of robots and environments based on the Unreal Tournament
game engine.
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.
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 two approaches 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. These approaches, termed long-term (LT) and short-term (ST) predictions, respectively, are based
on situation recognition and vehicle models for moving object prediction using sensor data. Our recent efforts
have demonstrated the ability to use the results of the short-term prediction algorithms to strengthen/weaken
the estimates of the long-term prediction algorithms. Based on previous experiments, we have found that the
short-term prediction algorithms perform best when predicting on the order of a few seconds into the future
and that the longer-term prediction algorithms are best at predicting on the order of several seconds into the
future. In this paper, we explore the time window in which both the short-term and the long-term prediction
algorithms provide reasonable results. Additionally, we describe a methodology by which we can determine
the time point at which the short-term prediction algorithm no longer provides results within an acceptable
predefined error threshold. 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.
The performance evaluation of an obstacle detection and segmentation algorithm for Automated Guided Vehicle (AGV) navigation in factory-like environments using a new 3D real-time range camera is the subject of this paper. Our approach expands on the US ASME B56.5 Safety Standard, which now allows for non-contact safety sensors, by performing tests on objects specifically sized in both the US and the British Safety Standards. These successful tests placed the recommended, as well as smaller, material-covered and sized objects on the vehicle path for static measurement. The segmented (mapped) obstacles were then verified in range to the objects and object size using simultaneous, absolute measurements obtained using a relatively accurate 2D scanning laser rangefinder. These 3D range cameras are expected to be relatively inexpensive and used indoors and possibly used outdoors for a vast amount of mobile robot applications building on experimental results explained in this paper.
A number of researchers have attempted to model human driving or flying skills (e.g., acceleration, steering, vehicle following, etc) in an effort to develop robotic or simulated driver models. In these applications, validation consists of comparing the source data to the model output; thus, it is assumed that model fidelity is correlated with similarity to true human performance. This paper reviews some of the validation metrics found in the literature and discusses the limitations of these metrics. It also presents an alternative metric designed to mitigate these limitations and the test-bed designed to derive this metric. Finally, it illustrates the application of this metric to a number of different trajectory models built through a variety of modeling techniques (e.g., Kalman filters, neural networks, and Newtonian equations).
In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multi-resolutional approach 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) and provides information to planners at the level of granularity that is appropriate for their planning horizon. The lower levels of the framework utilize estimation theoretic short-term predictions based upon an extended Kalman filter that provide predictions and associated uncertainty measures. The upper levels utilize a probabilistic prediction approach based upon situation recognition with an underlying cost model that provide predictions that incorporate environmental information and constraints. These predictions are made at lower frequencies and at a level of resolution more in line with the needs of higher-level planners. PRIDE is run in the systems’ world model independently of the planner and the control system. The results of the prediction are made available to a planner to allow it to make accurate plans in dynamic environments. 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.
The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured environment, the UGVs should have the difficult and computationally intensive competency to predict the future locations of moving objects that could interfere with its path. This paper details the development of a combined probabilistic object classification and estimation theoretic framework to predict the future location of moving objects, along with an associated uncertainty measure. The development of a moving object testbed that facilitates the testing of different representations and prediction algorithms in an implementation-independent platform is also outlined.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.