Name
Abstract | ||
|---|---|---|
The home environment introduces many challenges for robots to operate, for the home is much more unstructured than the environments in which industrial or commercial robots are found. This paper looks at a path planning problem and how to dynamically tune PID controllers that are to be used in the home environment. The method used for tuning is Learning Automata, specifically Finite Action Learning Automata for the prediction of the presence of people and a game of Continuous Action Learning Automata for the derivation of PID controllers. Results show that the proposed method efficiently derives better controllers for path planning when compared to a PID controller derived with a classical method. Furthermore, the method used to find acceptable waypoints shows that the robot is able to approximate the location of people in a home-care application. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00226 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Keywords | Field | DocType |
Home Robotics, Game Theory, Learning Automata, Machine Learning, FALA, CALA | Motion planning,Learning automata,PID controller,Computer science,Automaton,Action learning,Game theory,Artificial intelligence,Robot,Robotics,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Lindsay | 1 | 0 | 0.34 |
| Sidney Nascimento Givigi | 2 | 64 | 12.40 |