The article introduces a novel approach using neural implicit swept volume models to enhance collision detection in robotics. By leveraging deep learning techniques, the authors aim to accelerate collision detection and motion planning processes. The proposed model predicts signed distances for any point in the task space to the robot motion's swept volume, enabling efficient collision checks. The algorithm combines deep learning-based signed distance computations with geometric collision checkers to ensure accuracy while improving speed. Experimental validation in simulated and real-world robotic experiments demonstrates the effectiveness of the approach in enhancing a commercial bin picking application. The study highlights the potential of neural networks to streamline collision detection processes in various robotic applications.
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by Dominik Joho... at arxiv.org 03-14-2024
https://arxiv.org/pdf/2402.15281.pdfDeeper Inquiries