Raj Madhavan

Distributed Cooperative Outdoor Multirobot Localization and Mapping

The subject of this article is a scheme for distributed outdoor localization of a team of robots and the use of the robot team for outdoor terrain mapping. Localization is accomplished via Extended Kalman Filtering (EKF). In the distributed EKF-based scheme for localization, heterogeneity of the available sensors is exploited in the absence or degradation of absolute sensors aboard the team members. The terrain mapping technique then utilizes localization information to facilitate the fusion of vision-based range information of environmental features with changes in elevation profile across the terrain. The result is a terrain matrix from which a metric map is then generated. The proposed algorithms are implemented using field data obtained from a team of robots traversing an uneven outdoor terrain.

Distributed heterogeneous outdoor multi-robot localization

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.

Natural landmark-based autonomous vehicle navigation

Abstract This article describes a natural landmark navigation algorithm for autonomous vehicles operating in relatively unstructured environments. The algorithm employs points of maximum curvature, extracted from laser scan data, as point landmarks in an extended Kalman filter. A curvature scale space algorithm is developed to locate points of maximum curvature. The location of these points is invariant to view-point and sensor resolution. They can therefore be used as stable and reliable landmarks in a localization algorithm. 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 method developed is generally applicable to a range of unstructured environments and may be used with different types of sensors. The method is demonstrated as part of a navigation system for an outdoor vehicle in an unmodified operating domain.

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.

Autonomous underground navigation of an LHD using a combined ICP-EKF approach

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.

Natural landmark-based autonomous navigation using curvature scale space

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.

Distributed Heterogeneous Sensing for Outdoor Multi-Robot Localization, Mapping, and Path Planning

Our objective is to develop a team of autonomous mobile robots that are able to operate in previously unfamiliar outdoor environments. In these environments, the robot teams should be able to cooperatively localize even when DGPS is not consistently available, to autonomously generate rough elevation maps of their terrain, and to use these generated maps to plan multi-robot paths that enable them to accomplish their mission objective, such as reconnaissance and surveillance or perimeter security. This paper briefly outlines our approaches to achieving this objective, along with some of our implementation results on our team of four ATRV-mini mobile robots.

Incremental multi-agent robotic mapping of outdoor terrains

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.

Collaborative Robots for Infrastructure Security Applications

1 Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA, yguo1@stevens.edu, http://www.ece.stevens-tech.edu/ yguo 2 Department of Computer Science, University of Tennessee, Knoxville, TN 37996, USA, parker@cs.utk.edu, http://www.cs.utk.edu/ parker 3 Computational Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA, and Guest Researcher at the Intelligent Systems Division, National Institute of Standards & Technology (NIST), 100 Bureau Drive, Gaithersburg, MD 20899-8230, USA, raj.madhavan@ieee.org

Using 4D/RCS to address AI knowledge integration

In this article, we show how 4D/RCS incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying architecture. The 4D/RCS architecture is based on the supposition that different knowledge representation techniques offer different advantages, and 4D/RCS is designed in such a way as to combine the strengths of all of these techniques into a common unifying architecture in order to exploit the advantages of each. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that have been developed and applied and the value that each brings to achieving the ultimate goal of autonomous navigation. We also look at symbolic versus iconic knowledge representation and show how 4D/RCS accommodates both of these types of representations and uses the strengths of each to strive towards achieving human-level intelligence in autonomous systems.