Ashley Tews

MAPS : a system for multi-agent coordination

MAPS (Multi-Agent Planning System) is a system for multi-agent coordination that has been developed in the robot soccer domain. It uses potential fields to model the environment and provide directives for the robots. The key feature that enables successful coordination is the use of a shared world model. This paper describes MAPS and demonstrates it as a viable coordination system for use in multiple robot domains.

Thinking as one: coordination of multiple mobile robots by shared representations

Addresses issues in developing a coordination system for mobile robots in a hostile environment. Sharing a common representation of the environment improves the ability to plan future environment states. The multi-agent planning system (MAPS) is described that addresses these issues. Performance is examined in the highly dynamic robot soccer environment and demonstrates MAPS as a viable method of providing robot coordination.

UQ RoboRoos: Kicking on to 2000

The UQ RoboRoos have been developed to participate in the RoboCup robot soccer small size league. This paper overviews the history of the team, and provides details of some key factors to the teams success in 2000: a new goalkeeper design, robust communications and smooth, fast navigation. The paper concludes with some thoughts on the future of the RoboRoos.

3D payload detection from 2D range scans

Payload recognition is an important ability for autonomous industrial vehicles. While scanning laser range finders are commonly used on autonomous vehicles, most provide only 2D range scans. The goal of this research is to use the 2D range data to accurately identify an initially unknown 3D asymmetric payload. Typical approaches to payload recognition use either artificial markers or models with relatively tight constraints on the shape. We relax these constraints and allow the system to determine the most appropriate representation of the target object using an unsupervised learning approach. A set of target scan segments from the training set is reduced to a reference set with a high discrimination capability. The system uses the reference set in a classifier that evaluates incoming scans and monitors areas in the environment that potentially contain target objects. Upon a high enough confidence, a target is declared. Once trained, the system is able to accurately recognise a target object in different environments. The reduced reference set classifier shows faster convergence to a target classification than one developed with a full feature set and another with k-means clustering.

The UQ RoboRoos Small-Size League Team Description for RoboCup'98

The UQ RoboRoos have been developed to participate in the RoboCup 98 robot soccer small size league. This paper describes the current level of implementation of the robots, including aspects of hardware design, as well as the software running on the robots and the controlling computer. Key features of the RoboRoos design include the agile and powerful mechanical frame, the robots navigational techniques and a coordinating planner system based on potential field methods.