| 研究課題/領域番号 |
21K14115
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| 研究機関 | 東北大学 |
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研究代表者 |
Ravankar Ankit 東北大学, 工学研究科, 特任准教授 (40778528)
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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| キーワード | SLAM / Navigation / Deep Learning / Semantic Understanding / Path Planning / Robotics |
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| 研究実績の概要 |
This study aims to develop a lifelong semantic navigation system for mobile robots in dynamic environments by addressing two main problems, 1. Semantic understanding of everyday objects in indoor environments for long-term mapping and navigation, and 2. Planning under uncertainty for object search, detection, and exploration using multi-layered semantic maps. Data collection in simulated and real environments and tests for search-based optimized planning has been conducted. The project aims to enhance human-robot interaction in service robots by providing deep understanding of daily objects and their associations with locations, and developing an ontology for lifelong learning. This system can improve performance in tasks like search and exploration in dynamic settings.
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| 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
In the fiscal year, we implemented and tested the proposed framework in both simulated and real environments, using a house-like setting for data collection and testing various mapping and navigation algorithms. We completed the multi-layered semantic map pipeline and heuristic search and planning in diverse scenarios. Sensor integration on the robot system and deep learning training on common household objects were accomplished. The project resulted in a new multi-layer semantic mapping framework with metric maps, object maps, and search region maps within the simulated house.
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| 今後の研究の推進方策 |
In the next fiscal year, we plan to conduct a long-term mapping and navigation study within Tohoku University's living lab facility (a simulated house). We aim to enhance robot planning by adding new layers to the semantic map to account for human presence including layers for privacy and understanding social interactions for navigation. We also plan to extend the framework to incorporate multi-robots for improved object search in semantic maps and develop task allocation algorithms. Integration with IoT devices for speech-based interaction is also on the agenda. Lastly, we will test cutting-edge deep learning networks to optimize the power and performance efficiency of the navigation system.
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| 次年度使用額が生じた理由 |
For next FY we plan to attend several international conferences (IEEE-CASE23, IEEE-IROS23, IEEE-ICRA23, IEEE/SICE SII24) and publish the research results in journal magazines. Purchase PC related parts, GPU, and test new sensors for the tests in indoor scenarios. Some miscellaneous budget is required to purchase electrical and mechanical components, batteries for the robot, sensor replacement parts, and other experiment related items.
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