COBITT Datasets
COBITT is part of the DIH² (A Pan-European Network of Robotics DIHs for Agile Production.)
Skateboarding is a huge market (~$15bn) that has been experiencing increased interest and sales during the last years. There are many factors that add up to these trends, including the various competitions held such as Street League and X Games that have promoted the sport, as well as the inclusion of skateboarding in the 2020 Summer Olympics (held from 23 July to 8 August 2021 in Tokyo, Japan). In addition, skateboard manufacturers now follow effective and tailored-made marketing tactics to popularize their brands as a symbol on skateboards, a strategy that has attracted new consumers, particularly teenagers, due to their preference towards funky and attractive graphics. Moreover, companies put efforts in creating slogans and keywords that are popular in the market as a mean to derive effective sales. Despite the increased demand and sales in conventional skateboarding equipment and consumables, the global market has been stationary in terms of the introduction and implementation of new technologies that could improve the overall skateboarding experience for the user. This is expected to change, as technological breakthroughs throughout the global sports communities are being introduced more frequently, and innovative products are beginning to filter down to consumers. In addition, during the last two decades the industry has marginally matured and gained attention as alternative skateboarding solutions were introduced such as motorized skateboards, boards of different designs, and flexible boards.
Through the DIH2 project we aim to completely shift to a dynamic and agile manufacturing process, supported by FIWARE, digital, and robotized infrastructure. What needs to be further integrated in our current production line is a fully automated and agile process consisting of Two Transportation Robotic Platforms (a primary and a secondary), one custom-made combined 3 axis cartesian robotic laser cutter and one customised 4 axis cartesian robotic laser cutter/router.
This document describes two (2) COBITT scenarios that can be simulated/emulated using two (2) sets of datasets, respectively.
- Scenario 1: A production pipeline that utilises four (4) IoT/Robotic/CNC machines, which is in line with the production pipeline of a small factory (e.g., Capsule Skateboards Limited).
- Scenario 2: A production pipeline that utilises twelve (12) IoT/Robotic/CNC machines, which is in line with the production pipeline of a large factory.
Both datasets include two (2) types of csv. files:
- A (x1): encapsulates information about the orders placed through the COBITT Web App.
- B (x4 and x12, respectively): encapsulates logging information regarding individual machines operating at the factory level (as collected through the COBITT Rose-AP).
Both types of files are described below:
- A (Orders):
OrderDate
The date/time indicating when an order has been created/added in the system.
Name
The name of the order.
OrderStatus
The status of the order, i.e., if it is Pending, Failed or Successful.
MachineTimestamp
The timestamp indicating when a machine has started, was working or has finished its task.
MachineStatus
The active status of the machine (i.e., Started, Cutting, Trimming, Moving, Finished).
DeviceName
The name of the machine.
MachineType
The type of the machine (i.e., 3-Axis, 5-Axis, ROS2 Robot).
- B (Devices):
recvTime
The timestamp indicating when a machine has received a command or when a machine has sent data to FIWARE.
fiwareServicePath
The service path of FIWARE, which is "/" by default.
entityId
The unique ID of the entity/machine
entityType
The type of the entity/machine (namely, either IOT or ROS2).
attrName
The name of the attribute (i.e., the name of a command or a property like start, stop)
attrType
The type of the attribute (e.g., Text, Number).
attrValue
The value of the attribute, denoting the status of a machine (Trimming, Cutting, Started, Finished).