Robots and Physical Computing

I will start with a confession, I only had about 2 1/2 hours at BETT 2017  due to external time pressures so to say I didn't yet a chance for a good (or even a bad) look around is an understatement; so I am not reviewing the show just a few notes on what I did manage to see. STEAM Village First and mostly, it was great to talk to so many people, only few I had met face to face previously, about robots, micro:bits, Raspberry Pis and coding. Most of this happen in the relatively small (compared to the event space) STEAM village and nearby stalls. It was great to see the strong presence of both Raspberry Pi and Micro:Bit Foundation, along the variety of different activities and example usage of both, with Code Club (I know it is part of Raspberry Pi Foundation) there was well. This was all alongside some other companies Four of these stuck in my mind. 1. DFRobot ( https://www.dfrobot.com/ ) with their range of Arduino-based robots and non-programmable kits....

In the last post 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). More details can be found in the previous post. 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 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] Modifications to the code in the  last post  have been around fine tuning the values in c onverting the outputs of the two neurons S1 and S2 into actions as shown below.     if s1=...

In the   last post I was playing with 4Tronix's   Bit: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:       ...