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This research advances the reliability of soft robotics by developing a fault-tolerant control framework that maintains structural rigidity and workspace even when actuators fail. By integrating discrete-time control barrier functions with kinematic optimization, the study mathematically guarantees safe operation under degraded conditions, a critical step for deploying inflatable truss robots in unstructured environments.
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Fault-Tolerant, Rigidity-Preserving Control of Inflatable Truss Robots

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arXiv:2605.20561v2 Announce Type: replace Abstract: Isoperimetric robotic trusses can adapt to different tasks and environments due to their high strength-to-weight ratio and the ability to reconfigure dramatically into new shapes. However, motor failures in operational environments can severely limit capability if not properly addressed. This paper presents a fault-tolerant control framework for an inflatable robotic truss that maintains functionality despite motor failures, demonstrated through three key contributions. First, we extend the kinematic optimization to handle arbitrary combinations of motor failures by imposing equality constraints to ensure failed actuators are not used. Second, we introduce discrete-time control barrier function (DTCBF) constraints that, under the assumed kinematic model, mathematically guarantee structural rigidity while maximizing workspace utilization. Third, we implement closed-loop position control using onboard encoder feedback and a forward-kinematics-based state estimator, improving positional accuracy in the presence of disturbances. We validate our approach through simulation and hardware experiments on a 2D isoperimetric truss testbed. For a 2D configuration with 6 actuators, we demonstrate >62% workspace preservation under single-motor failures, a >22% reduction in tracking RMSE using failure-aware closed-loop control relative to the fully functional open-loop baseline, and a 39% closed-loop RMSE reduction over failure-unaware open-loop control under unmodeled partial actuator degradation. These results establish a foundation for more robust and resilient isoperimetric truss robots operating under degraded actuation.

Abstract:Isoperimetric robotic trusses can adapt to different tasks and environments due to their high strength-to-weight ratio and the ability to reconfigure dramatically into new shapes. However, motor failures in operational environments can severely limit capability if not properly addressed. This paper presents a fault-tolerant control framework for an inflatable robotic truss that maintains functionality despite motor failures, demonstrated through three key contributions. First, we extend the kinematic optimization to handle arbitrary combinations of motor failures by imposing equality constraints to ensure failed actuators are not used. Second, we introduce discrete-time control barrier function (DTCBF) constraints that, under the assumed kinematic model, mathematically guarantee structural rigidity while maximizing workspace utilization. Third, we implement closed-loop position control using onboard encoder feedback and a forward-kinematics-based state estimator, improving positional accuracy in the presence of disturbances. We validate our approach through simulation and hardware experiments on a 2D isoperimetric truss testbed. For a 2D configuration with 6 actuators, we demonstrate >62% workspace preservation under single-motor failures, a >22% reduction in tracking RMSE using failure-aware closed-loop control relative to the fully functional open-loop baseline, and a 39% closed-loop RMSE reduction over failure-unaware open-loop control under unmodeled partial actuator degradation. These results establish a foundation for more robust and resilient isoperimetric truss robots operating under degraded actuation.

8 pages, 9 figures, 1 table. Accepted to IEEE Robotics and Automation Letters (RA-L)

Robotics (cs.RO)

arXiv:2605.20561 [cs.RO]

(or arXiv:2605.20561v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.20561 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/LRA.2026.3733614 Focus to learn more DOI(s) linking to related resources

supplementary_materials.mp4

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