Skip to main content

cutest computational thinking in the world?

Wonder Workshop (https://www.makewonder.com/) produce the  robots Dash and Dot robots (see picture above). It is hard not to be charmed by these robots, they are cute, easy to use, download the Apps and you are ready to go almost out of the box - and add to this an easy to use but fairly powerful tool for developing programming.


At the time of writing the software is only available for IOS but there are plans for Android. 

Blockly, available as one of apps, can be used to program the robots. It is a simple looking graphical language (simpler looking but similar to Scratch). A simple example (shown opposite) where Dash (the bigger of the two) does things such as  moves forward,  going left, lights change to orange, , left ear changes colour, head moves forward and it roars like a dinosaur. It relatively easy to then add loops and test (such as checking if it's 'friend' Dot is in view). Below is a very short video of Dash moving around until it 'sees' Dot.





It is difficult not to anthropomorphise these, especially when they are left alone they try and attract your attention with noises. They are just fun as well.

Recently, other developers have been producing alternative programming approaches. The Tickle App (https://tickleapp.com/en-us/) has added these robots to their supported devices.





All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with.

Popular posts from this blog

Micro:bit, Servo control with Micropython or blocks

You can control servos (small ones) from a Micro:Bit directly. Following a link from the David Whale (Twitter ) , thank you, took me to a Kitronik blog post, https://www.kitronik.co.uk/blog/using-bbc-microbit-control-servo/, which has the answer.

The code uses Microsoft Blocks taken from the post, runs the servos 180 degrees and back again, when button A is pressed. It does exactly what it should. I am also using the Tower Pro SG90 servo.
Can it be replicated in Micropython? This is a new mini project, there seems to be little out there yet on how do this but the best so far is this video by PHILG2864:



The closest I have is the following, it is essentially there.
from microbit import *
pin0.set_analog_period(20)
while True:
    pin0.write_analog(180)
    sleep(1000)
    pin0.write_analog(1)
    sleep(1000)

Setting the time period to 20ms  pin0.set_analog_period(20)seems by experiment (and used in the video above) to be best value so far. The reason for pin0.write_analog(1)  set to 1 i…

mbots - graphical programming and Arduino

Makeblock (http://mblock.cc/mbot/) funded through Kickstarter the development of a new robot - mBot (https://www.kickstarter.com/projects/1818505613/mbot-49-educational-robot-for-each-kid) with the subtitle "$49 educational robot for each kid". What they came up with is a interesting system that uses their mBlock software, which resembles Scratch but produces code for Arduino, to program a robot with LEDs, light sensors and buzzer integrated on the main board; but also comes with sensors for line-following, ultrasonic sensor and with the version in the kickstarter reward a 16x8 LED matrix.

My impression so far it is really quite intuitive to work with, in the example above the robot:

moves forward;displays 'f' on the LED matrix; turns right;displays 'r' on the LED matrix;repeats until the on-board is pressed to stop the motors. 

What I like most though is seeing the graphical code turned into Arduino code - the potential to see the same thing done into two ways…

4Tronix Bit:Bot Neuron Controlled Edge follower

In thelast post I was playing with 4Tronix'sBit:Bot. In this post I will show the initial experimentation with an artificial neuron controlling the Bit:Bot to follow the edge of a line (it follows the left-hand side of the line).


The neurons (well two separate ones, S1 and S2) are produced using weighted sums - summing the weights x inputs [ right-hand sensor (rs) and left-hand sensor (ls)] plus a bias for each neuron in this case w[0] and w[3].







    net=w[0]+w[1]*rs+w[2]*ls           net2=w[3]+w[4]*rs+w[5]*ls

  If weighted sum >=0 then its output 1 otherwise 0 if net>=0:          s1=1     else:         s1=0
    if net2>=0:         s2=1     else:         s2=0
What actual causes S1 to be either 1 or 0 is all defined by a set of weights w (three for the first neurone, S1,  three for S2).
w=[0,-1,1,-1,1,-1]


Converting the outputs of the two neurones S1 and S2 into actions is shown below.