Timothy Wiley

University of New South Wales, Kensington, New South Wales, Australia

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Publications (9)1.43 Total impact

  • T. Wiley · C. Sammut · I. Bratko
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    ABSTRACT: This paper resolves previous problems in the Multi-Strategy architecture for online learning of robotic behaviours. The hybrid method includes a symbolic qualitative planner that constructs an approximate solution to a control problem. The approximate solution provides constraints for a numerical optimisation algorithm, which is used to refine the qualitative plan into an operational policy. Introducing quantitative constraints into the planner gives previously unachievable domain independent reasoning. The method is demonstrated on a multi-tracked robot intended for urban search and rescue. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
    No preview · Article · Jan 2014
  • T. Wiley · C. Sammut · I. Bratko
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    ABSTRACT: Qualitative Simulation (QSIM) reasons about the behaviour of dynamic physical systems as they evolve over time. The system is represented by a coarse qualitative model rather than precise numerical models. However, for large complex domains, such as robotics for Urban Search and Rescue, existing QSIM implementations are inefficient. ASPQSIM is a novel formulation of the QSIM algorithm in Answer Set Programming that takes advantage of the similarities between qualitative simulation and constraint satisfaction problems. ASPQSIM is compared against an existing QSIM implementation on a variety of domains that demonstrate ASPQSIM provides a significant improvement in efficiency especially on complex domains, and producing simulations in domains that are not solvable by the procedural implementation.
    No preview · Article · Jan 2014 · Frontiers in Artificial Intelligence and Applications
  • Timothy Wiley · Claude Sammut · Ivan Bratko

    No preview · Conference Paper · Aug 2013
  • Timothy Wiley · Claude Sammut · Ivan Bratko

    No preview · Conference Paper · Jan 2013
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    ABSTRACT: Advances in sensing technology and algorithm design make it possible for a robot equipped with a laser range-finder to generate a map and localise itself within the map as the robot explores its environment.We describe a system for mapping and virtual reconstruction developed as part of a robot for urban search and rescue. The process of mapping and, at the same time, localising the robot within the map, is called Simultaneous Localisation and Mapping (SLAM). In many applications, such as urban search and rescue, information from wheel encoders is inaccurate and cannot be used for odometry to obtain a position estimate. However, iterative closest point scan matching algorithms make it possible for a robot to perform accurate positioning in unstructured environments where wheel slip is common. When this positioning is combined with a mapping algorithm such as FastSLAM, the robot can construct an accurate map in real-time as it moves. Given the generated map and the robot's position within it, a variety of exploration algorithms allow the robot to autonomously explore its environment. The robot is also equipped with an RGB-D camera. The 3D information as well as the colour video images are incorporated into the map to produce a 3D virtual reconstruction of the environment as the robot explores. This robot won the award for best autonomous robot in three successive RoboCup Rescue Robot competitions [1], 2009 - 2011.
    No preview · Conference Paper · Nov 2012

  • No preview · Conference Paper · Jul 2012
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    ABSTRACT: Robots are used for Urban Search and Rescue to assist rescue workers. To enable the robots to find victims, they are equipped with various sensors including thermal, video and depth time-of-flight cameras, and laser range-finders. We present a method to enable a robot to perform this task autonomously. Thermal features are detected using a dynamic temperature threshold. By aligning the thermal and time-offlight camera images, the thermal features are projected into 3D space. Edge detection on laser data is used to locate holes within the environment, which are then spatially correlated to the thermal features. A decision tree uses the correlated features to direct the autonomous policy to explore the environment and locate victims. The method was evaluated in the 2010 RoboCup Rescue Real Robots Competition.
    No preview · Conference Paper · Jun 2012
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    ABSTRACT: The primary purpose of rescue robots is to locate victims within a disaster environment, either to report their position or to deliver aid. Although there exist several solutions for mapping and localizing a robot in an unknown environment, they often require an estimate of the robot's motion. In a disaster site, a robot might not have effective motion encoders, either because of the discontinuous nature of the terrain or because the robot's locomotion devices do not remain in constant contact with the ground. We have developed an algorithm for tracking the position of a robot in a disaster environment and generating a map that functions in real time. Our solution is based on a range finder device and is robust to temporary errors in the range scan. By aligning each scan to an occupancy grid of prior scan data, we can find the robot's position more accurately than current techniques that align only to the previous scan. The accurate position tracking allows us to more efficiently implement a solution to the mapping problem. In addition, our solution can track the position of the robot and generate a map based on three-dimensional scan data, instead of requiring that the range sensor be fixed in a level plane. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.
    No preview · Article · Nov 2011 · Journal of Field Robotics
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    ABSTRACT: Many applications for robotics require that the robot know its current position in the environment. While there exist several solutions for localizing a robot, even in a previously unknown environment, they often require an estimate of the robot's motion. However, in many situations, a robot may not have motion encoders, or its encoders may be highly inaccurate. We have developed an algorithm for tracking the position of a robot, based on a rangeflnder device, that is robust to temporary errors in the range scan. By aligning each scan to an occupancy grid of prior scan data, we can find the robot's position more accurately than current techniques which only align to the previous scan. In addition, our solution can track the position of the robot based on three dimensional scan data, instead of requiring that the range sensor be fixed in a level plane.
    No preview · Conference Paper · May 2011