Name
Abstract | ||
|---|---|---|
Simultaneous Search and Monitoring (SSM) is studied in this paper for a single Unmanned Aerial Vehicle (UAV) searcher and multiple moving ground targets. Searching for unknown targets and monitoring known targets are two intrinsically related problems, but have mostly been addressed in isolation. We combine the two problems with a joint objective function in a Partially Observable Markov Decision Process (POMDP). An online policy planning approach is proposed to plan a reactive policy to solve the POMDP, using both Monte-Carlo sampling and Simulated Annealing. The simulation result shows that the searcher will successfully find unknown targets without losing known ones. We demonstrate, with a theoretical proof and comparative simulations, that the proposed approach can deliver a better performance than conventional foresight optimization methods. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) | Simulated annealing,Mathematical optimization,Markov process,Computer science,Partially observable Markov decision process,Probability distribution,Sampling (statistics),Linear programming |
DocType | ISSN | Citations |
Conference | 0743-1546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoyu Zhang | 1 | 0 | 0.34 |
| Sandor M Veres | 2 | 89 | 18.37 |
| Andreas Kolling | 3 | 232 | 20.20 |