“Eating your own dogfood”, aka dogfooding, refers the practice of a company testing it’s own products by using them internally. At a research day held by Somerset College, a quote in a talk by Lorna Sheppard on Len Deighton’s cookbooks (yes, that Len Deighton…) from a 2014 Observer magazine article (Len Deighton’s Observer cookstrips, Michael Caine and the 1960s) caught my attention:
[G]enerally, you stand a better chance of succeeding in something if whatever you create, you also like to consume.
Implicit in this is the idea that you are also creating for a purpose.
In the OU engineering residential school currently running at the University of Bath, one of the four day long activities the students engage with is a robotics activity using Lego EV3 robots, where at each stage we try to build in a reason for adding another programming construct or learning how to work with a new sensor. That is, we try to motivate the learning by making it purposeful.
The day is structured around a series of challenges that allow students to develop familiarity with programming a Lego EV3 robot, adding sensors to it, logging data from the sensors and then interpreting the data. The activities are contextualised by comparing the work done on the Lego EV3’s with the behaviour of a Roomba robot vacuum cleaner – by the end of the morning, students will have programmed their robot to perform the majority of the Roomba’s control functions, including finding it’s way home to a homing beacon, as well as responding to touch (bumper), colour (line stopper) and proximity (infra-red and ultrasonic) sensors.
The day concludes with a challenge, where an autonomous robot must enter – and return from – a closed tunnel network, using sensors to collect data about the internal structure of the tunnel, as well identifying the location of a casualty who has an infra-red emergency beacon with them.
(The lids are placed on the tunnels so the students can’t see inside.)
As well as the partition walls (which are relocated each time the challenge is run, so I’m not giving anything away!), pipework and cables (aka coloured tape) also run through the tunnel and may be mapped by the students using a downward facing light sensor.
The casualty is actually a small wooden artist’s mannequin – the cuddly teddy we used to use does not respond well to the ultrasound sensor the students use to map the tunnel.
The data logged by the students include motor rotation data to track the robots progress, ultrasonic sensor data to map the walls, infra-red sensor data to find the emergency beacon and a light sensor to identify the cables/pipework.
The data collected looks something like this:
The challenge is then to map the (unseen by the students) tunnel network, and tell the robot’s story from the data.
The result is a narrative that describes the robot’s progress, and a map showing the internal structure of the tunnel:
If time allows, this can then be used as the basis for programming the robot to complete a rescue mission!
The strategies used by the students to log the data, and control the robot to send it into the tunnel and retrieve it safely again, are based on what they learned completing the earlier challenges set throughout the day.