| 研究課題/領域番号 |
21K14115
|
|---|
| 研究機関 | 東北大学 |
|---|
研究代表者 |
Ravankar Ankit 東北大学, 工学研究科, 特任講師 (40778528)
|
|---|
| 研究期間 (年度) |
2021-04-01 – 2024-03-31
|
| キーワード | Navigation / Mobile Robots / Deep Learning / Semantic Mapping / Scene Understanding / Path Planning / SLAM |
|---|
| 研究実績の概要 |
This study aims to realize a lifelong navigation system for mobile robots in dynamic environments by considering semantic information. Trials were conducted using several sensors in simulation and real environments. In the simulation, modeling of everyday household items and essential features using computer vision were studied and applied to the simulation environment for feature extraction and deep learning training. Several datasets in dynamic scenarios were collected and tested using sensor fusion algorithms. Sensor selection and assembly of actual robot hardware was completed. Deep learning training for detecting and extracting semantic information from the scene graph is currently being studied. Moreover, tests in real-environment have started. Several results were published in conferences and journals.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
This study aims to realize the long-term navigation of robots in challenging dynamic scenarios by considering deep semantic information. Despite the COVID pandemic that severely impacted and delayed the procurement of crucial robot components and primary sensors, we were able to test many of the algorithms inside the simulation environment. Furthermore, we conducted an evaluation study to determine appropriate sensors and actuators for the robot by analyzing several sensor parameters in the simulation. Based on obtained results, experiments on real-robot is planned for the next FY.
|
| 今後の研究の推進方策 |
For the FY2022, the plan is to develop the simulation model and real hardware concurrently and start experiments in real scenarios with actual robots. Additional sensors that were unavailable in the previous FY are planned to be procured and tested for data acquisition. To realize the long-term navigation for mobile robots, tests in several dynamic scenarios, including low-light conditions and durations (several days to weeks), are planned with continuous data acquisition. The later stage of this FY aims to train a robust deep learning framework to study dynamic changes in the environment by utilizing the hybrid metric-topological framework described in the original submitted proposal.
|
| 次年度使用額が生じた理由 |
For experiments in challenging dynamic scenarios, we will procure additional sensors attached to the robot hardware, such as a stereo camera, motion capture system, motion trackers, and 3D Lidars. Additionally, we will train the dataset on deep learning workstations (GPU). Miscellaneous expenses include reserving experiment rooms, travel funds for international conferences, open access publication fees, conference registrations, and other expenses such as consumables for mechanical and electrical components required for the robot hardware development and computing units.
|
