READ ME File For 'Data for Self-organised communication-aware control structures for robot swarms' Dataset DOI: 10.5258/SOTON/D3561 ReadMe Author: Thomas Graham Kelly, University of Southampton 0000-0002-7356-4099 This dataset supports the thesis entitled Self-organised communication-aware control structures for robot swarms AWARDED BY: University of Southampton DATE OF AWARD: 2025 Date of data collection: 08/2021 - 05/2025 Information about geographic location of data collection: Licence: CC-BY Related projects/Funders: MINDS CDT -------------------- DATA & FILE OVERVIEW -------------------- This dataset contains: Chapter 3 - Contains the raw data from simulations of communication-aware collective decision making contained in the folder "Chapter 3 raw data". The plotted figures used in the thesis contained in the folder "Chapter 3 plots". The python scripts required to read the raw data and plot the graphs found in the thesis. Chapter 4 - Contains the raw data from simulations examining the trade-offs of hybrid control structures for robot swarms contained in "Chapter 4 raw data". This folder also contains run files for the experiments outlining the parameters used for each experiment. The plotted figures used in the thesis contained in the folder "Chapter 4 plots". The python scripts required to read the raw data and plot the graphs found in the thesis. The scripts for the corresponding figures in the thesis are named as such. Chapter 5 - Contains the raw data from simulations of Dynamic hybrid control structures contained in "Chapter 5 raw data". This folder also contains run files for the experiments outlining the parameters used for each experiment. The plotted figures used in the thesis contained in the folder "Chapter 5 plots". The python scripts required to read the raw data and plot the graphs found in the thesis. The scripts for the corresponding figures in the thesis are named as such. Simulation files - Contains the python files of the simulation environments and corresponding agent classes that were used to generate the above raw data. "run.py" and "agent.py" was used to generate data for Chapter 3 while "runHierarchical.py" and "agentHierarchical.py" were used to generate data for Chapters 4 and 5. "utils.py" contains helper functions for generating the environments and running the simulations. -------------------------- METHODOLOGICAL INFORMATION -------------------------- Raw data is stored in a serialised form using the Pickle Python 3.8.5 library. The raw data is stored as a dictionary that contains time series data such as agent positions at each timestep, the number of messages sent, their internal states and who they were communicating with. To process this data, the Pickle file can be read in using a Python script, as is done in the plot files contained in the data set. Once read, a dictionary can be populated with the data. Each metric contained in the dictionary can be accessed via the key corresponding to the metric. The raw data is generally quite large and so it is advised that you read one raw data file at a time. -------------------------- DATA-SPECIFIC INFORMATION -------------------------- Files contained in folder "Chapter 3" Number of variables: 6 Number of cases/rows: 20 Variable list, defining any abbreviations, units of measure, codes or symbols used: Estimates - Agents estimate of the state of the environment at any time step Beliefs - Their beliefs over the state of the environment affected by their neighbours Decision made - Whether the agent has made a decision over the state of the environment Messages sent - Number of messages sent at a time step Coverage (m^2) - The coverage of the swarm at a timestep Missing data codes: N/A Specialized formats or other abbreviations used: N/A Date that the file was created: Aug, 2021 Files contained in folder "Chapter 4" and "Chapter 5" Number of variables: 24 Number of cases/rows: 20 Variable list, defining any abbreviations, units of measure, codes or symbols used: "Decent Attend Times" - Time taken in seconds to observe an event for decentralised swarm "Hierarchical Attend - Times" Time taken in seconds to observe an event for a hierarchical swarm "Decent Complete Times" - Time taken to complete an event for a decentralised swarm "Hierarchical Complete Times" - Time taken to complete an event for a hierarchical swarm "Decent Counts" - Number of agents present for a decentralised swarm "Hierarchical Counts" - Number of agents present for a hierachical swarm "Decent Messages" - Number of messages sent to an operator by a decentralised swarm "Hierarchical Messages" - Number of messages sent to an operator by a hierarchical swarm "Decent Messages Total" - Number of messages sent to other members of the swarm by a decentralised swarm "Hierarchical Messages Total" - Number of messages sent to other members of the swarm by a hierarchical swarm "Leaders Removed Times": - Time step in seconds when an agent may have been removed "Hierarchical Energy Spent" - Energy spent by a hierarchical swarm "Decent Energy Spent" - Energy spent by a decentralised swarm "Hierarchical Coverage Total" - Total coverage of a hierarchical swarm in m^2 "Decent Coverage Total" - Total coverage of a decentralised swarm in m^2 "Hierarchical Largest Team Coverage" - Largest total coverage of a hierarchical swarm with a team in m^2 "Hierarchical Largest Team Size" - Number of agents in the largest hierarchical team "Hierarchical Agents in Teams" - Number of hierarchical agents that are in any team "Hierarchical No of Teams" - Number of distinct hierarchical teams "Hierarchical Agent Positions" - Positions of agents in a hierarchical swarm "Decent Agent Positions" - Positions of agents in a decentralised swarm "Time Leader Removed" - Timestep in seconds where a swarm leader was removed "Hier Events Metric" - Data pertaining to events in the hierarchical experiments including when the event was spawned and when it was observed and completed "Decent Events Metric" - Data pertaining to events in the decentralised experiments including when the event was spawned and when it was observed and completed Missing data codes: N/A Specialized formats or other abbreviations used: N/A Date that the file was created: Jan, 2023 --------------