Configuration Space Decomposition for Scalable Proxy Collision Checking in Robot Planning and Control
| Autor: | Mrinal Verghese, Nikhil Das, Yuheng Zhi, Michael Yip |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science::Robotics Human-Computer Interaction Computer Science - Robotics Control and Optimization Artificial Intelligence Control and Systems Engineering Mechanical Engineering Biomedical Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) ComputingMethodologies_COMPUTERGRAPHICS Computer Science Applications |
| Zdroj: | IEEE Robotics and Automation Letters. 7:3811-3818 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2022.3147458 |
| Popis: | Real-time robot motion planning in complex high-dimensional environments remains an open problem. Motion planning algorithms, and their underlying collision checkers, are crucial to any robot control stack. Collision checking takes up a large portion of the computational time in robot motion planning. Existing collision checkers make trade-offs between speed and accuracy and scale poorly to high-dimensional, complex environments. We present a novel space decomposition method using K-Means clustering in the Forward Kinematics space to accelerate proxy collision checking. We train individual configuration space models using Fastron, a kernel perceptron algorithm, on these decomposed subspaces, yielding compact yet highly accurate models that can be queried rapidly and scale better to more complex environments. We demonstrate this new method, called Decomposed Fast Perceptron (D-Fastron), on the 7-DOF Baxter robot producing on average 29x faster collision checks and up to 9.8x faster motion planning compared to state-of-the-art geometric collision checkers. 8 pages, 9 figures, Accepted to IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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