Author name cluster

Ashley Tews

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

IROS Conference 2012 Conference Paper

Pedestrian detection in industrial environments: Seeing around corners

Paulo V. K. Borges
Ashley Tews
Dave Haddon

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments initially occluded or obscured dynamic objects can unpre dictably enter the path of a vehicle. We propose a safety system which integrates a vision-based offboard pedestrian tracking subsystem with an onboard localisation and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controllers field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. We implemented and tested the proposed solution on an automated industrial vehicle to verify the applicability for both human drivers and under autonomous operation.

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IROS Conference 2011 Conference Paper

3D payload detection from 2D range scans

Ashley Tews

Payload recognition is an important ability for autonomous industrial vehicles. While scanning laser rangefinders 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.

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ICRA Conference 2010 Conference Paper

Vision-based localization using an edge map extracted from 3D laser range data

Paulo V. K. Borges
Robert Zlot
Michael Bosse
Stephen T. Nuske
Ashley Tews

Reliable real-time localization is a key component of autonomous industrial vehicle systems. We consider the problem of using on-board vision to determine a vehicle's pose in a known, but non-static, environment. While feasible technologies exist for vehicle localization, many are not suited for industrial settings where the vehicle must operate dependably both indoors and outdoors and in a range of lighting conditions. We extend the capabilities of an existing vision-based localization system, in a continued effort to improve the robustness, reliability and utility of an automated industrial vehicle system. The vehicle pose is estimated by comparing an edge-filtered version of a video stream to an available 3D edge map of the site. We enhance the previous system by additionally filtering the camera input for straight lines using a Hough transform, observing that the 3D environment map contains only linear features. In addition, we present an automated approach for generating 3D edge maps from laser point clouds, removing the need for manual map surveying and also reducing the time for map generation down from days to minutes. We present extensive localization results in multiple lighting conditions comparing the system with and without the proposed enhancements.

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IROS Conference 2008 Conference Paper

WiFi position estimation in industrial environments using Gaussian processes

Felix Duvallet
Ashley Tews

The increased popularity of wireless networks has enabled the development of localization techniques that rely on WiFi signal strength. These systems are cheap, effective, and require no modifications to the environment. In this paper, we present a WiFi localization algorithm that generates WiFi maps using Gaussian process regression, and then estimates the global position of an autonomous vehicle in an industrial environment using a particle filter. This estimate can be used for bootstrapping a higher-resolution localizer, or for cross-checking and localization redundancy. The system has been designed to operate both indoors and outdoors, using only the existing wireless infrastructure. It has been integrated with an existing laser-beacon localizer to aid during initialization and for recovery after a failure. Experiments conducted at an industrial site using a large forklift-type autonomous vehicle are presented.

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ICRA Conference 2007 Conference Paper

Autonomous Hot Metal Carrier

Ashley Tews
Cédric Pradalier
Jonathan Roberts 0001

This paper reports work involved with the automation of a hot metal carrier - a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demonstrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8 km and conducted 60 load handling operations. We also describe an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network.

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ICRA Conference 2007 Conference Paper

Autonomous Hot Metal Carrier - Navigation and Manipulation with a 20 tonne industrial vehicle

Jonathan Roberts 0001
Ashley Tews
Cédric Pradalier
Kane Usher

This paper reports work on the automation of a Hot Metal Carrier, which is a 20 tonne forklift-type vehicle used to move molten metal in aluminium smelters. To achieve efficient vehicle operation, issues of autonomous navigation and materials handling must be addressed. We present our complete system and experiments demontrating reliable operation. One of the most significant experiments was five-hours of continuous operation where the vehicle travelled over 8km and conducted 60 load handling operations. Finally, an experiment where the vehicle and autonomous operation were supervised from the other side of the world via a satellite phone network are described.

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ICRA Conference 2004 Conference Paper

A Multi-robot Approach to Stealthy Navigation in the Presence of an Observer

Ashley Tews
Gaurav S. Sukhatme
Maja J. Mataric

We propose a simple, reactive method for multiple robots carrying out sequential low-visibility navigation in the presence of an observer. Initially, the robots have no map of the environment but know the locations of the observer and goal. They generate an occupancy grid representation of the environment which is modeled using potential fields with embedded task information. These fields are combined and navigation waypoints extracted. Each robot carries out its traverse independently and shares its experience with its successor. The experience information consists of the occupancy grid and a filtered version of the traveled path used to assist the subsequent robot to traverse a lower visibility path. This produces a robust and reactive solution for stealthy navigation since there is no global path planning and the robots are not committed to any particular path. Experiments in simulation and real outdoor environments substantiate the approach and demonstrate the benefits of sharing information in reducing cumulative visibility. The experiments also demonstrate the algorithm's versatility in taking advantage of an environment that changes between robot traverses.

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IROS Conference 2004 Conference Paper

Avoiding detection in a dynamic environment

Ashley Tews
Maja J. Mataric
Gaurav S. Sukhatme

Remaining elusive while navigating to a goal in a dynamic environment containing an observer requires taking advantage of opportunistic cover as it occurs. A reactive navigation approach is needed that recognizes the utility of environment features in offering protective cover. We present an approach that allows stealthy traverses in unknown environments containing dynamic objects. It is a frontier-based method that allows a robot to follow in the obscuring shadow of objects despite their dynamics, and take advantage of more opportunistic cover if it becomes available. An analysis of our approach in off-line modeling and experiments conducted in simulation and outdoor environments demonstrate its effectiveness in achieving high quality solutions for stealthy navigation.

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ICRA Conference 2003 Conference Paper

A scalable approach to human-robot interaction

Ashley Tews
Maja J. Mataric
Gaurav S. Sukhatme

Much of the current research in human-robot interaction is concerned with single systems and single or few users. These systems and their interfaces are generally tightly-coupled and well-defined. For large-scale human-robot applications, the systems may be unknown prior to designing the interface for potential human interaction. This presents a difficult goal for allowing multiple users to interact with many possibly unknown systems. In this paper, we present an interaction infrastructure aligned with providing this interface. It operates in two phases that accommodate both many-to-many interaction and generalized, one-to-one interaction between users and robotic systems. Our previous research has demonstrated the infrastructure to scale to a large number of users and several systems in simulation. The experiments in this paper substantiate these results in a smaller-scale real robotic environment.

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IROS Conference 2000 Conference Paper

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

Ashley Tews
Gordon F. Wyeth

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.

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