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Robot Collision Detection Based on Deep Lagrangian Network Integrated Momentum Observer

Yan Li

Abstract


In human-robot collaboration (HRC), real-time collision detection is essential to prevent harm. Existing methods often require extra
sensors or complex experiments. To address this, we propose a momentum observer-based collision detection method that identifies external
torques caused by collisions. It integrates a deep Lagrangian network (DeLaN) to model robot dynamics without needing parameter identification or prior knowledge of robot structure. The DeLaN model is trained on real robot data and performs well. Experiments confirm the method
effectively detects collisions.

Keywords


Momentum observer; Deep Lagrangian network; Collision detection

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DOI: http://dx.doi.org/10.70711/aitr.v4i1.9649

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Scineer Publishing is a publisher of open access journals. Covering various disciplines and promoting interdisciplinary development, Scineer Publishing adheres to a principle of respecting academics, and publishes insightful and rigorous academic research contents.

12 Eu Tong Sen Street, #07-168, Singapore 059819
E-mail: scineerpub@126.com

Scineer

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Copyright © 2023 Scineer Publishing Pte. Ltd.