Demo Data from VLDB 2026 / reviewers in the wild / expert
Eric Benson
dblp:14/2387
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › multi-robot control
multi-robot formation planning |
0.0 | 1 | 2003 | Planning and control of UGV formations in a dynamic environment: a practical framework with experiments · ICRA 2003 |
Robotics › Motion planning and robot control
trajectory planning |
0.0 | 1 | 2003 | Planning and control of UGV formations in a dynamic environment: a practical framework with experiments · ICRA 2003 |
Methods — techniques the papers use, named apart from their topics
lyapunov-based control · 0.0dijkstra's search · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Planning and control of UGV formations in a dynamic environment: a practical framework with experimentsabstractThis paper provides a practical framework for planning and control of formations of multiple unmanned ground vehicles to traverse between goal points in a dynamic environment. This framework allows online planning of the formation paths using a Dijkstra's search algorithm based on the current sensor data. The formation is allowed to dynamically change in order to avoid obstacles in the environment while minimizing a cost function aimed at obtaining collision-free and deadlock-free paths. Based on the feasible path for a virtual leader of the group, the trajectory planner satisfies the kinematic constraints of the individual vehicles while accounting for inter-vehicle collision and path constraints. A Lyapunov based controller is designed to keep the vehicles on their planned trajectories. Illustrative simulations of groups of unmanned ground vehicles and their laboratory implementation with three unmanned ground vehicles are presented. Yongxing Hao, Benjamin Laxton, Sunil K. Agrawal, Eric Benson |
ICRA | 5 |