Name
Abstract | ||
|---|---|---|
In the field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SRDS.2017.42 | 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS) |
Keywords | Field | DocType |
Fault-tolerant,Multi-roobt system,Pattern formation,Reinforcement learning | Robot learning,Robot control,Computer science,Robot kinematics,Pattern formation,Fault tolerance,Robot,Actuator,Distributed computing,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
1060-9857 | 978-1-5386-1680-2 | 1 |
PageRank | References | Authors |
0.37 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Wang | 1 | 79 | 17.75 |
| Jiannong Cao | 2 | 5226 | 425.12 |
| Shan Jiang | 3 | 14 | 2.09 |