EDBT 2026 Demo / reviewers in the wild / expert
Shengqiang Chen
dblp:383/4478
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › constraint optimization
mixed-integer linear programming |
0.8 | 1 | 2024 | 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 |
0.8 | 1 | 2024 | Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot Teams · ICRA 2024 |
Robotics › Robot manipulation
cooperative task execution |
0.2 | 1 | 2024 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Accounting for Travel Time and Arrival Time Coordination During Task Allocations in Legged-Robot TeamsabstractMany 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 |
ICRA | 1 |