3D-CNN Based Heuristic Guided Task-Space Planner for Faster Motion Planning
| Autor: | Ryo Terasawa, Yuka Ariki, Kenichiro Nagasaka, Takuya Narihira, Toshimitsu Tsuboi |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Mobile manipulator Heuristic Computer science Deep learning 020208 electrical & electronic engineering 02 engineering and technology Convolutional neural network 020901 industrial engineering & automation Path (graph theory) 0202 electrical engineering electronic engineering information engineering Robot Artificial intelligence Motion planning Heuristics business |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra40945.2020.9196883 |
| Popis: | Motion planning is important in a wide variety of applications such as robotic manipulation. However, it is still challenging to reliably find a collision-free path within a reasonable time. To address the issue, this paper proposes a novel framework which combines a sampling-based planner and deep learning for faster motion planning, focusing on heuristics. The proposed method extends Task-Space Rapidly-exploring Random Trees (TS-RRT) to guide the trees with a "heuristic map" where every voxel has a cost-to-go value toward the goal. It also utilizes fully convolutional neural networks (CNNs) for producing more appropriate heuristic maps, rather than manually-designed heuristics. To verify the effectiveness of the proposed method, experiments for motion planning using a real environment and mobile manipulator are carried out. The results indicate that it outperforms the existing planners, especially in terms of the average planning time with smaller variance. |
| Databáze: | OpenAIRE |
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