Name
Abstract | ||
|---|---|---|
A scenario of ground agent classifying scattered objects of interest is introduced. Objects of interests are arbitrarily oriented and placed in a contested area. Ground agent with sensor capabilities is moving around each object to collect measurements and classify this object. Agent is required to collect as much measurements as needed to minimize its probability of misclassifying this object. In addition, path optimization condition is imposed on agent's path. As it might be typical in these scenarios, agent might classify same object differently depending on agent's position with respect to object's location and orientation. In this paper, we formally introduce and formulate this problem. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 AMERICAN CONTROL CONFERENCE (ACC) | Motion planning,Computer vision,Kinematics,Computer science,Azimuth,Artificial intelligence,Classifier (linguistics) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 8 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mariam Faied | 1 | 2 | 1.74 |