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3D-CNN Based Heuristic Guided Task-Space Planner for Faster Motion Planning

Date
2020
Academic Conference
International Conference on Robotics and Automation (ICRA)
Authors
Ryo Terasawa
Yuka Ariki
Takuya Narihira
Toshimitsu Tsuboi
Kennichiro Nagasaka (Sony Corporation)
Research Areas
Robotics

Abstract

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 Rapidlyexploring 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.

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