Name
Abstract | ||
|---|---|---|
In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations. A video describing our method and results can be found here. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IROS40897.2019.8968483 | 2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Field | DocType | ISSN |
Motion planning,Computer vision,Obstacle,Computer science,Workspace,Artificial intelligence,Robot,Trajectory,Robot planning,Reinforcement learning,Trajectory planning | Conference | 2153-0858 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arbaaz Khan | 1 | 24 | 4.64 |
| Chi Zhang | 2 | 0 | 0.34 |
| Shuo Li | 3 | 0 | 0.68 |
| Jiayue Wu | 4 | 0 | 0.34 |
| Brent Schlotfeldt | 5 | 1 | 1.16 |
| Sarah Tang | 6 | 20 | 2.47 |
| Alejandro Ribeiro | 7 | 15 | 6.75 |
| Osbert Bastani | 8 | 3 | 4.09 |
| Vijay Kumar | 9 | 7086 | 693.29 |