Name
Abstract | ||
|---|---|---|
The presence of children in a social assistive robotics context is particularly challenging for perception, mainly, in the task of locating them using inherently uncertain sensor data. This paper proposes a method for active perception with the goal of finding one target, e.g., a child wearing a RFID tag. This method is based on a particle-filter modeling a probability distribution of the position of the child. Negative measurements are used to update this probability distribution and an information-theoretic approach to determine optimal robot trajectories that maximize information gain while surveying the environment. We present preliminary results, in a real robot, to evaluate the approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25554-5_45 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Computer vision,Social robot,Robot control,Active perception,Computer science,Particle filter,Probability distribution,Artificial intelligence,Robot,Robotics,Mobile robot | Conference | 9388 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
6 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João V. Messias | 1 | 26 | 4.77 |
| José J. Acevedo | 2 | 0 | 0.34 |
| Jesús Capitán | 3 | 7 | 2.88 |
| L. Merino | 4 | 264 | 19.87 |
| Rodrigo Ventura | 5 | 104 | 25.64 |
| Pedro U. Lima | 6 | 516 | 69.88 |