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Pengming Zhu

dblp:273/1509 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-2440-5331ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 2 since 2021Systems, architecture and hardware · 1 · 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 · 100%
Theoretical computer science
1 paper
Distributed computing theory · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › multi-robot control
multi-robot formation control
0.912025
Versatile Distributed Maneuvering With Generalized Formations Using Guiding Vector Fields · ICRA 2025
Robotics › Motion planning and robot control › path following
vector field guidance
0.912025
Versatile Distributed Maneuvering With Generalized Formations Using Guiding Vector Fields · ICRA 2025

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

nonholonomic control · 1.7guiding vector field · 1.7consensus theory · 1.7
YearPublicationVenuePosition
2025 Versatile Distributed Maneuvering With Generalized Formations Using Guiding Vector Fields
abstract
This paper presents a unified approach to realize versatile distributed maneuvering with generalized formations. Specifically, we decompose the robots' maneuvers into two independent components, i.e., interception and enclosing, which are parameterized by two independent virtual coordinates. Treating these two virtual coordinates as dimensions of an abstract manifold, we derive the corresponding singularity-free guiding vector field (GVF), which, along with a distributed coordination mechanism based on the consensus theory, guides robots to achieve various motions (i.e., versatile maneuvering), including (a) formation tracking, (b) target enclosing, and (c) circumnavigation. Additional motion parameters can generate more complex cooperative robot motions. Based on GVFs, we design a controller for a nonholonomic robot model. Besides the theoretical results, extensive simulations and experiments are performed to validate the effectiveness of the approach.
Sha Luo, Pengming Zhu, Weijia Yao, Héctor García de Marina, Xin Xu 0001
ICRA3
2022 Robot navigation in a crowd by integrating deep reinforcement learning and online planning
Zhiqian Zhou, Pengming Zhu, Junhao Xiao 0001, Huimin Lu 0002, Zongtan Zhou
Appl. Intell.2