| Project/Area Number |
18H01391
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 20010:Mechanics and mechatronics-related
|
|---|
| Research Institution | Utsunomiya University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
山仲 芳和 宇都宮大学, 工学部, 助教 (00804238)
嶋脇 聡 宇都宮大学, 工学部, 教授 (10344904)
|
|---|
| Project Period (FY) |
2018-04-01 – 2021-03-31
|
| Project Status |
Completed (Fiscal Year 2020)
|
| Budget Amount *help |
¥9,750,000 (Direct Cost: ¥7,500,000、Indirect Cost: ¥2,250,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥7,670,000 (Direct Cost: ¥5,900,000、Indirect Cost: ¥1,770,000)
|
|---|
| Keywords | 自転車 / ふらつき走行 / 動的モデル / パラメータ同定 / ゆらぎ / 確率密度関数 / 走行実験 / 数理モデル / 予測 / モデル化 |
|---|
| Outline of Final Research Achievements |
In this study, we successfully measured the roll angular displacement, wheel's lateral displacement, steering angular displacement, and each velocity. Next, using their probability density functions (PDFs) as training data, the model parameters were accurately identified. The resulting PDF fitnesses were over 97% for all participants, indicating that our simulated PDFs reproduced the human PDFs. Moreover, we successfully developed a new algorithm based on Ito's stochastic analysis and achieved faster calculations without Mote-Calro methods. Furthermore, we successfully developed an expanded version of particle swarm optimization (PSO) reduces the number of trials of the optimization.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果により，自転車のふらつき振幅が及ぶ範囲を予測するための動的モデルと，これを実験データに適合させるパラメータ同定アルゴリズムが確立した．得られたモデルは，自転車のふらつき振幅を予測するためのキーテクノロジーとなり，対自転車の事故を防止する自動運転自動車の実現に寄与する．また，もう１つの成果として，提案手法の効率化や高速化に資する独自アルゴリズムの開発にも，それぞれ成功した．これは，提案手法を車載コンピュータ上にリアルタイム実装する際の基盤技術となる．
|
