Observability-Informed Optimal Sensor Placement for Soft Robots

2026-04-07

International Conference on Soft Robotics

AI Summary

This study addresses the lack of generalizability and the neglect of system dynamics in sensor placement for soft robots by introducing linear control theory to this domain for the first time. We propose an interpretable, generalized optimization framework that formulates a convex optimization problem to maximize the observability Gramian, thereby determining optimal sensor configurations. The resulting layout is integrated with Kalman filtering to achieve accurate state estimation. Experimental validation on a continuum manipulator demonstrates that the proposed approach significantly reduces position and strain reconstruction errors compared to baseline methods, attaining millimeter-level accuracy and confirming its effectiveness.

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.

Problem

• soft robots
• optimal sensor placement
• observability
• system dynamics
• state estimation

Innovation

• Optimal Sensor Placement
• Soft Robots
• Observability Gramian
• Convex Optimization
• Kalman Filter