Name
Title | ||
|---|---|---|
A Heuristic Learning Algorithm for Preferential Area Surveillance by Unmanned Aerial Vehicles. |
Abstract | ||
|---|---|---|
A heuristic learning algorithm is presented in this paper to solve the problem of persistent preferential surveillance by an Unmanned Aerial Vehicle (UAV). The algorithm helps a UAV perform surveillance as per quantitative priority specifications over a known area. It allows the specification of regional priorities either as percentages of visitation to be made by a UAV to each region or as percentages of surveillance time to be spent within each. Additionally, the algorithm increases the likelihood of target detection in an unknown area. The neighborhood of a detected target is suspected to be a region of a high likelihood of target detection, and the UAV plans its path accordingly to verify this suspicion. Similar to using the target information, the algorithm uses the risk information to reduce the frequency of visits to risky regions. The technique of using risk map to avoid risky regions is adapted from the existing geometric reinforcement learning technique. The effectiveness of this algorithm is demonstrated using simulation results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10846-017-0498-5 | Journal of Intelligent and Robotic Systems |
Keywords | Field | DocType |
Persistent surveillance, Preferential surveillance, Learning, UAV, Target detection, MSC 68T05, MSC 68T20 | Heuristic,Algorithm,Risk map,Engineering,Reinforcement learning | Journal |
Volume | Issue | ISSN |
88 | 2-4 | 1573-0409 |
Citations | PageRank | References |
2 | 0.38 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manickam Ramasamy | 1 | 2 | 0.38 |
| Debasish Ghose | 2 | 777 | 71.57 |