| Project/Area Number |
17H07391
|
|---|
| Research Category |
Grant-in-Aid for Research Activity Start-up
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Intelligent robotics
|
|---|
| Research Institution | National Institute of Advanced Industrial Science and Technology |
|---|
Principal Investigator |
Kumagai Iori 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (60803880)
|
|---|
| Research Collaborator |
Kanehiro Fumio
Morisawa Mitsuharu
Nakaoka Shin'ichiro
|
|---|
| Project Period (FY) |
2017-08-25 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | ヒューマノイドロボット / 環境計測 / 全身動作計画 / 経路計画 / 障害物回避 / 移動計画 / 環境記憶 / 足配置計画 |
|---|
| Outline of Final Research Achievements |
In this research, we developed the adaptive locomotion planning system for humanoid robot to pass through an unknown complex environment utilizing its human-like body structure. We achieved global footstep planning and online footstep compensation with collision avoidance and ground surface estimation even in an occluded scenario using accumulated environmental memories. We also solved the problem of increasing computational cost in the whole-body locomotion planning by performing a sequential whole-body collision avoidance while approximating obstacles as primitive convex shapes and utilizing the result of the footstep planning as the guide. Furthermore, this motion planning strategy, which used a low-dimensional planning result as a guide, was extended to multi-contact posture planning and contributed to the expansion of the movable range of a humanoid robot.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究の学術的意義は, 周囲環境計測に基づくモデル獲得と身体構造を用いた干渉回避・多点接触姿勢計画を統合することで, 事前情報のない複雑環境におけるヒューマノイドロボットの移動可能範囲を拡大したことである.
環境計測情報を記憶することで視界の制約がある複雑環境においても大域移動計画と移動誤差の補正が可能になるとともに, 多自由度な身体構造に起因した全身動作計画の計算コスト増大の課題を低次元の計画結果をガイドとする段階的な動作計画手法により解決したことは, ヒューマノイドロボットが既存の人間の作業環境を変えることなく重労働を代行するために必須の技術であり実用上の意義も大きいと言える.
|
