READ ME File For 'Biologically Inspired Underwater Soft Robotics: Enhanced force production and disturbance rejection Dataset'

Dataset DOI: 10.5258/SOTON/D3190

ReadMe Author: Leo Micklem, University of Southampton [0000-0001-7682-4370]

This dataset supports the thesis entitled 'Biologically Inspired Underwater Soft Robotics: Enhanced force production and disturbance rejection'
AWARDED BY: University of Southampton
DATE OF AWARD: 2024

DESCRIPTION OF THE DATA
This dataset contains:

Setpoint_Data.zip: Data Pertaining to Chapter 4. Contains MetaData, Processing Code, and data text files
Tunable-stiffness_foil_data.zip: Data pertaining to Chapter 3. Contains 4 Excel spreadsheets including data for dynamic and static testing.
Open_loop_triangle_wave_disturbance A-D .zip: Data pertaining to chapter 5. Contains MetaData, Processing code, .mat force data 
2022-12-17_Force_Testing_ A-B.zip: Data pertaining to chapter 5. Contains .mat force data
2023-01-11_Force_Testing.zip:Data pertaining to chapter 5. Contains .mat force data
2022-06-01_Force_Testing.zip:Data pertaining to chapter 5. Contains .mat force data
2022-06-02_PIV_Testing.zip:Data pertaining to chapter 5. Contains .mat force data
2024-09-03_Matrix_and_processing.zip: Processing and MetaData for chapter 5 
Square_wave_-_all_data.zip: Data Pertaining to Chapter 5. Contains metadata, processing code, .mat force data, and camber text files 

Description of methods used for collection/generation of data: All methods documented in PhD Thesis Biologically Inspired Underwater Soft Robotics: Enhanced force production and disturbance rejection

Methods for processing the data: Processing scripts included in dataset

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: MATLAB version 2019 or later, Python3

Environmental/experimental conditions: University of Southampton Recirculating Water Tunnel

Describe any quality-assurance procedures performed on the data: Repeat trials and error analysis using standard deviation and means.

People involved with sample collection, processing, analysis and/or submission: Leo Micklem, Huazhi Dong, Gabriel D. Weymouth, Francesco Giorgio-Serchi, Blair Thornton, Yunjie Yang

Date of data collection: 2021-08-21 - 2024-03-02

Information about geographic location of data collection: 
University of Southampton, Southampton, UK; University of Edinburgh, Edinburgh, UK

Licence:
CC BY

Related projects/Funders:
UK Research Institute Grant EP/T517859/1

Related publication:
L.Micklem, G.D.Weymouth, B.Thornton
Energy-Efficient Tunable-Stiffness Soft Robots using second moment of area actuation.
IEEE/RSJ International Conference on Intelligent Robots
10.1109/IROS47612.2022.9981704

Date that the file was created: September, 2024