Experimental data and results for the I-SUPPORT two-module soft robotic arm, focusing on assistive bathing tasks. The dataset includes identification data from an Intel RealSense depth camera, step responses under load, position tracking in vertical/horizontal configurations, orientation control, and task execution data like sponge rubbing. MATLAB scripts for modeling, controller design, and analysis are provided by author Carlos Relaño, with metadata last updated in November 2025.
Use Cases
- Training or benchmarking system identification models based on camera data of tendon and McKibben chamber actuation.
- Developing and testing position (X,Z) and orientation (pitch, yaw) control algorithms for soft robotic arms.
- Evaluating the performance of fractional-order controllers (FOPI) for compliant, nonlinear systems.
- Reproducing and analyzing experiments for load disturbance rejection and task execution like sponge rubbing.
Strengths
- Includes experimental results for multiple validation scenarios: position tracking, orientation regulation, load disturbance rejection, and task execution.
- Provides MATLAB scripts for modeling, controller design, data collection, and plotting, enabling experiment reproduction.
- Data was collected using an Intel RealSense depth camera, suggesting precise spatial measurements.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count, file formats, and dataset size are unknown, which may limit suitability assessment.
Provenance
- Source
- Relaño, Carlos via e-cienciaDatos Harvested Dataverse
- Collection Method
- Data collected from experiments with a two-module soft robotic arm using an Intel RealSense camera.
- Time Range
- null
- Freshness
- Last updated 2025-11-16 05:27:31; freshness should be verified.
- Geography
- null