Fangyi Zhang
Queensland University of Technology
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Publication
Featured researches published by Fangyi Zhang.
intelligent robots and systems | 2015
Kejie Qiu; Fangyi Zhang; Ming Liu
For mobile robots and position-based services, such as healthcare service, precise localization is the most fundamental capability while low-cost localization solutions are with increasing need and potentially have a wide market. A low-cost localization solution based on a novel Visible Light Communication (VLC) system for indoor environments is proposed in this paper. A number of modulated LED lights are used as beacons to aid indoor localization additional to illumination. A Gaussian Process(GP) is used to model the intensity distributions of the light sources. A Bayesian localization framework is constructed using the results of the GP, leading to precise localization. Path-planning is hereby feasible by only using the GP variance field, rather than using a metric map. Dijkstras algorithm-based path-planner is adopted to cope with the practical situations. We demonstrate our localization system by real-time experiments performed on a tablet PC in an indoor environment.
IEEE Robotics & Automation Magazine | 2016
Kejie Qiu; Fangyi Zhang; Ming Liu
We propose to use visible-light beacons for low-cost indoor localization. Modulated light-emitting diode (LED) lights are adapted for localization as well as illumination. The proposed solution consists of two components: light-signal decomposition and Bayesian localization.
international conference on robotics and automation | 2015
Fangyi Zhang; Kejie Qiu; Ming Liu
Indoor localization is a fundamental capability for service robots and indoor applications on mobile devices. To realize that, the cost and performance are of great concern. In this paper, we introduce a lightweight signal encoding and decomposition method for a low-cost and low-power Visible Light Communication (VLC)-based indoor localization system. Firstly, a Gold-sequence-based tiny-length code selection method is introduced for light encoding. Then a correlation-based asynchronous blind light-signal decomposition method is developed for the decomposition of the lights mixed with modulated light sources. It is able to decompose the mixed light-signal package in real-time. The average decomposition time-cost for each frame is 20 ms. By using the decomposition results, the localization system achieves accuracy at 0.56 m. These features outperform other existing low-cost indoor localization approaches, such as WiFiSLAM.
computer vision and pattern recognition | 2017
Fangyi Zhang; Jürgen Leitner; Michael Milford; Peter Corke
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuomotor policies (modular networks) where each module is trained independently. Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.
conference on automation science and engineering | 2015
Kejie Qiu; Fangyi Zhang; Ming Liu
For mobile robots and position-based services, localization is the most fundamental capability while path-planning is an important application based on that. A novel localization and path-planning solution based on a low-cost Visible Light Communication (VLC) system for indoor environments is proposed in this paper. A number of modulated LED lights are used as beacons to aid indoor localization additional to illumination. A Gaussian Process (GP) is used to model the intensity distributions of the light sources. Path-planning is hereby feasible by using the GP variance field, rather than using a metric map. Graph-based path-planners are introduced to cope with the practical situations. We demonstrate our path-planning system by real-time experiments performed on a tablet PC in an indoor environment.
international conference on robotics and automation | 2017
Jürgen Leitner; Adam W. Tow; Niko Sünderhauf; Jake E. Dean; Joseph W. Durham; Matthew Cooper; Markus Eich; Christopher Lehnert; Ruben Mangels; Christopher McCool; Peter T. Kujala; Lachlan Nicholson; Trung Pham; James Sergeant; Liao Wu; Fangyi Zhang; Ben Upcroft; Peter Corke
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems — including perception and manipulation — instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.
arXiv: Learning | 2015
Fangyi Zhang; Jürgen Leitner; Michael Milford; Ben Upcroft; Peter Corke
ARC Centre of Excellence for Robotic Vision; School of Electrical Engineering & Computer Science; Faculty of Science and Technology | 2017
Fangyi Zhang; Jürgen Leitner; Michael Milford; Peter Corke
Archive | 2016
Fangyi Zhang; Jürgen Leitner; Ben Upcroft; Peter Corke
Archive | 2015
Kejie Qiu; Fangyi Zhang; Ming Liu
Collaboration
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Dalle Molle Institute for Artificial Intelligence Research
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