Name
Abstract | ||
|---|---|---|
This paper presents an optimization strategy to coordinate a fleet of Automated Guided Vehicles (AGVs) traveling on ad-hoc pre-defined roadmaps. Specifically, the objective is to maximize traffic throughput of AGVs navigating in an automated warehouse by minimizing the time AGVs spend negotiating complex traffic patterns to avoid collisions with other AGVs. In this work, the coordination problem is posed as a Quadratic Program where the optimization is performed in a centralized manner. The proposed method is validated by means of simulations and experiments for different industrial warehouse scenarios. The performance of the proposed strategy is then compared with a recently proposed decentralized coordination strategy that relies on local negotiations for shared resources. The results show that the proposed coordination strategy successfully maximizes vehicle throughput and significantly minimizes the time vehicles spend negotiating traffic under different scenarios. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10514-018-9730-9 | Autonomous Robots |
Keywords | Field | DocType |
Multi-robot coordination, Quadratic optimization, AGV systems, Path planning | Motion planning,Coordination game,Mathematical optimization,Computer science,Simulation,Throughput,Quadratic programming | Journal |
Volume | Issue | ISSN |
43.0 | 3 | 1573-7527 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valerio Digani | 1 | 52 | 4.94 |
| M. Ani Hsieh | 2 | 382 | 34.69 |
| Lorenzo Sabattini | 3 | 393 | 36.65 |
| Cristian Secchi | 4 | 977 | 81.94 |