Shengqiang Chen

dblp:383/4478 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—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
Multi-agent systems · 44% Planning, search and constraint satisfaction · 44% Robot manipulation · 13%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › constraint optimization
mixed-integer linear programming
0.812024
Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot Teams · ICRA 2024
Knowledge, reasoning and agents › Multi-agent systems
task allocation
0.812024
Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot Teams · ICRA 2024
Robotics › Robot manipulation
cooperative task execution
0.212024
Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot Teams · ICRA 2024

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

mixed-integer linear programming · 0.8
YearPublicationVenuePosition
2024 Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot Teams
abstract
Many applications require the deployment of legged-robot teams to effectively and efficiently carry out missions. The use of multiple robots allows tasks to be executed concurrently, expediting mission completion. It also enhances resilience by enabling task transfer in case of a robot failure. This paper presents a formulation based on Mixed Integer Linear Programming (MILP) for allocating tasks to robots by taking into account travel time and ensuring efficient execution of collaborative tasks. We extended the MILP formulation to account for complexities with legged robot teams. Our results demonstrate that this approach leads to improved performance in terms of the makespan of the mission. We demonstrate the usefulness of this approach using a case study involving the disinfection of a building consisting of multiple rooms.
Shengqiang Chen, Ronak Jain, Xiaopan Zhang, Quan Nguyen 0004, Satyandra K. Gupta
ICRA1