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This paper proposes a new method to measure the pose and localize the contacts with the surrounding environment for an inflatable soft robot by using optical sensors (photocells), inertial measurement units (IMUs), and a pressure sensor. These affordable sensors reside entirely aboard the robot and will be effective in environments where external sensors, such as motion capture, are not feasible to use. The entire bore of the robot is used as a waveguide to transfer the light. When the robot is working, the photocell signals vary with the current shape of the robot and the IMUs measure the orientation of its tip. Analytical functions are developed to relate the photocell signals and the robot pose. Since the soft robot is deformable, the occurrence of contact at any location on its body will modify the sensor signals. This simple measurement approach generates enough information to allow contact events to be detected and classified with high precision using a machine learning algorithm.
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This content will become publicly available on March 12, 2026
A Configuration Sensing Approach for Fabric-Reinforced Inflatable Soft Robots Based on Interior Illumination
Not AvailablePose measurement and contact realization for soft robots are important but are also challenging to perform. In this article, a new approach is proposed which uses light transmission through the bore of the robot to sense its pose. By combining optical signals with inertial measurement units and pressure signals, along with governing models and machine learning algorithms, this sensing approach allows us to measure the pose of the soft robot in free space. It also allows the applied machine learning algorithm to estimate the robot configuration during contact events, predicting the location, direction, and magnitude of the contact force by learning from prior contact data in a training phase. The sensing devices used are affordable and widely available so that the robot can be mass-produced. Experimental results show that the proposed sensing system successfully estimates these quantities with good accuracy.
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- Award ID(s):
- 1935312
- PAR ID:
- 10657787
- Publisher / Repository:
- Mary Ann Liebert
- Date Published:
- Journal Name:
- Robotics Reports
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2835-0111
- Page Range / eLocation ID:
- 12 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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