A
Anqi Xu
Researcher at McGill University
Publications - 37
Citations - 1115
Anqi Xu is an academic researcher from McGill University. The author has contributed to research in topics: Robot & Robot control. The author has an hindex of 18, co-authored 31 publications receiving 864 citations. Previous affiliations of Anqi Xu include Hong Kong Polytechnic University.
Papers
More filters
Proceedings ArticleDOI
Optimal complete terrain coverage using an Unmanned Aerial Vehicle
TL;DR: This work introduces a system that applies and extends a generic algorithm to achieve automated terrain coverage using an aerial vehicle, and extensive experimental results in simulation validate the presented system.
Proceedings ArticleDOI
Multi-domain monitoring of marine environments using a heterogeneous robot team
Florian Shkurti,Anqi Xu,Malika Meghjani,Juan Camilo Gamboa Higuera,Yogesh Girdhar,Philippe Giguère,Bir Bikram Dey,Jimmy Li,Arnold Kalmbach,Chris Prahacs,Katrine Turgeon,Ioannis Rekleitis,Gregory Dudek +12 more
TL;DR: A heterogeneous multi-robot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems, is described, comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot.
Proceedings ArticleDOI
OPTIMo: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations
Anqi Xu,Gregory Dudek +1 more
TL;DR: Evaluated results highlight OPTIMo’s advances in both prediction accuracy and responsiveness over several existing trust models, making possible the development of autonomous robots that can adapt their behaviors dynamically, to actively seek greater trust and greater efficiency within future human-robot collaborations.
Journal ArticleDOI
Efficient complete coverage of a known arbitrary environment with applications to aerial operations
TL;DR: A novel algorithm is introduced, based on the boustrophedon cellular decomposition technique, for computing an efficient complete coverage path for a known environment populated with arbitrary obstacles.
Posted Content
Adversarial Examples in Modern Machine Learning: A Review
TL;DR: An extensive coverage of machine learning models in the visual domain is provided, furnishing the reader with an intuitive understanding of the mechanics of adversarial attack and defense mechanisms and enlarging the community of researchers studying this fundamental set of problems.