To conduct experiments1 with large groups of robots you have to pay for large groups of robots. Specifically, you must pay for the number of robots multiplied by the cost per single robot. And, what is the easiest way to keep the cost per single robot low? You make them simple. Unfortunately, simple robots constrain the type of experiments you can perform. Therefore, you want large groups of smart robots without paying for their smartness!
In our most recent publication, we demonstrate how low-cost hardware of simple robots can be used beyond its standard functionality. We show how magnetic field readings from the single Hall-effect sensor on our HoverBots can be used to detect: i) rotations; ii) successful movements; and iii) collisions. One of the core tools we used in this research is the instrument model. The instrument model was developed by the sensing and measurement community to help understand the physics and information transfer in sensors, and in our case we used this model to study the circumstances under which sensors become multi-functional. A key understanding that we have derived from studying the instrument model is that, in swarm robotic systems, the existing sensors could often be further utilised, and therefore the systems should be reanalysed. Our publication describes a generalisable strategy that other researchers could use to increase the capability of their own swarm robots without modifying their hardware.
This publication also demonstrates one of my personal views on research, that is, we as a research community are sometimes better advised on spending our creativity on things that already exist rather than on things that do not. There is an entire world of fascinating stuff out there that, if at all, has been viewed from one angle and eagerly awaits to be discovered from many others. This is most definitely the reason why I fell in love with multi-disciplinary research. The borders between different scientific fields become opportunities; researchers are given the unique chance to look at problems in one field from the perspective of another.
1(of course, in the real world and not in silico)
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