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Kanzhong Yao

dblp:351/4118 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 87% Robot navigation and mapping · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › multi-robot control
leader-follower control
0.712023
Image-Based Visual Servoing Switchable Leader-follower Control of Heterogeneous Multi-agent Underwater Robot System · ICRA 2023
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.712023
Image-Based Visual Servoing Switchable Leader-follower Control of Heterogeneous Multi-agent Underwater Robot System · ICRA 2023
Robotics › Robot navigation and mapping › mobile robot navigation › vehicle navigation
underwater vehicle navigation
0.212023
Image-Based Visual Servoing Switchable Leader-follower Control of Heterogeneous Multi-agent Underwater Robot System · ICRA 2023

Methods — techniques the papers use, named apart from their topics

image-based visual servoing · 0.7
YearPublicationVenuePosition
2023 Image-Based Visual Servoing Switchable Leader-follower Control of Heterogeneous Multi-agent Underwater Robot System
abstract
Confined and cluttered aquatic environments present a number of significant challenges with respect to inspection by robotic platforms, including localisation and communications. Some of these can be mitigated by using collaborative heterogeneous multi-robot teams. An important element of such a system is collaborative control. This paper addresses this challenge by presenting an Image-Based Visual Servoing (IBVS), leader-follower control system for heterogeneous aquatic robots. Experiments were conducted in an uncluttered pond to demonstrate the capabilities of the system. The results show robots can maintain tracking each other with maximum$x$and$y$displacements of 0.42 m and 0.41 m, the maximum projection distance in the xy-plane of maintaining formation is 0.45 m, showing the stability and feasibility of deploying such system on underwater platforms.
Kanzhong Yao, Nathalie Bauschmann, Thies L. Alff, Wei Cheah, Daniel-André Duecker, Keir Groves, Ognjen Marjanovic, Simon Watson 0001
ICRA1