Acoustic Sensing for Universal Jamming Grippers
Lion Weber, Theodor Wienert, Martin Splettst\"o{\ss}er, Alexander Koenig, Oliver Brock

TL;DR
This paper introduces acoustic sensing integrated into universal jamming grippers, enabling high-resolution, material, and object discrimination without compromising grasping compliance, validated through real-world object sorting tasks.
Contribution
It presents a novel acoustic morphological sensing method embedded in jamming grippers, preserving compliance while achieving high-resolution sensing and object discrimination.
Findings
High spatial resolution in size and orientation sensing
Robust object and material discrimination with high accuracy
Successful real-world object sorting with continuous grasping
Abstract
Universal jamming grippers excel at grasping unknown objects due to their compliant bodies. Traditional tactile sensors can compromise this compliance, reducing grasping performance. We present acoustic sensing as a form of morphological sensing, where the gripper's soft body itself becomes the sensor. A speaker and microphone are placed inside the gripper cavity, away from the deformable membrane, fully preserving compliance. Sound propagates through the gripper and object, encoding object properties, which are then reconstructed via machine learning. Our sensor achieves high spatial resolution in sensing object size (2.6 mm error) and orientation (0.6 deg error), remains robust to external noise levels of 80 dBA, and discriminates object materials (up to 100% accuracy) and 16 everyday objects (85.6% accuracy). We validate the sensor in a realistic tactile object sorting task,…
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Taxonomy
TopicsAdvanced Sensor and Energy Harvesting Materials · Robot Manipulation and Learning · Soft Robotics and Applications
