Observability-Informed Optimal Sensor Placement for Soft Robots

Abstract:This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods either lack generalizability across different soft robot morphologies or do not account for system dynamics. The applied method uses convex optimization to find the optimal sensor configuration that maximizes an observability Gramian-based metric. The framework is experimentally evaluated using position and strain measurements on a soft continuum arm. Kalman filter state estimates using optimal sensor placements yield lower reconstruction error than a baseline across all sinusoidal input trials, with improvements on the order of millimeters. This case study shows that linear control theory tools can guide optimal sensor placement in soft robots, suggesting an interpretable approach to sensor placement that may extend to other morphologies.

Published in IEEE Robosoft 2026. 8 pages, 4 figures, 6 tables

Robotics (cs.RO)

arXiv:2609.33681 [cs.RO]

(or arXiv:2609.33681v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.33681 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proc. IEEE Int. Conf. Soft Robotics (RoboSoft) 2026, pp. 430-437

https://doi.org/10.1109/RoboSoft67810.2026.11522828 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.