Safety System of Drone Operation based on On-line Identification of the Human Operator
| Project/Area Number |
26420810
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Aerospace engineering
|
|---|
| Research Institution | Kanazawa University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2014-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
|
|---|
| Keywords | 操縦者モデル / リアルタイム同定 / 操縦者状態 / リアルタイムモデリング / 注視点モデル / オペレータモデル / 無人航空機 / オペレータ状態 / 操縦シミュレータ / ワークロード推定 |
|---|
| Outline of Final Research Achievements |
The estimation algorithm of the operator condition using identified operator mode was discussed. The operator models of the drone and self-driving car were identified as a response model to the vehicle motion and the condition change around the car such as a distance to the forward car. The output of the operator model are control behavior and the movement of the viewpoint. These model structures enable to analyze the inner condition of the human operator.
The flight and driving simulator were conducted and the operating experiments of a drone and self-driving car with and without subtask were performed. The relations between the identified model and the workload imposed by the subtask were analyzed. The result indicated that the residual and the model variations of the identified model have correlations with the workload.
|
Report
(4 results)
Research Products
(3 results)
