From Prof. Alan Winfield's Web Log
Although exhausting, it was at the same time uplifting. We had a crowd of very interested families and children the whole time - in fact the organisers tell me that Robotville had just short of 8000 visitors over the 4 days of the exhibition. What was really nice was that the whole exhibition was hands-on, and our sturdy e-puck robots - at pretty much eye-level for 5-year olds, attracted lots of small hands interacting with the swarm. A bit like putting your hand into an ants nest (although I doubt the kids would have been so keen on that.)
Let me explain what the robots were doing. Paul had programmed two different demonstrations, one with fixed behaviours and the other with learning.
For the fixed behaviour demo the e-puck robots were programmed with the following low-level behaviours:
- Short-range avoidance. If a robot gets too close to another robot or an obstacle then it turns away to avoid it.
- Longer-range attraction. If a robot can sense other robots nearby but gets too far from the flock, then it turns back toward the flock. And while in a flock, move slowly.
- If a robot loses the flock then it speeds up and wanders at random in an effort to regain the flock (i.e. another robot).
- While in a flock, each robot will communicate (via infra-red) its estimate of the position of an external light source to nearby robots in the flock. While communicating the robot flashes its green body LED.
- Also while in a flock, each robot will turn toward the 'consensus' direction of the external light source.
The net effect of these low-level behaviours is that the robots will both stay together as a swarm (or flock), and over time, move as a swarm toward the external light source. Both of these swarm-level behaviours are emergent because they result from the low-level robot-robot and robot-environment interactions. While the flocking behaviour is evident in just a few minutes, the overall swarm movement toward the external light source is less obvious. In reality even the flocking behaviour appears chaotic, with robots losing each other, and leaving the flock, or several mini-flocks forming. The reason for this is that all of the low-level behaviours make use of the e-puck robots' multi-purpose Infra-red sensors, and the environment is noisy; in other words because we don't have carefully controlled lighting there is lots of ambient IR light constantly confusing the robots.
The learning demo is a little more complex and makes use of an embedded evolutionary algorithm, actually running within the e-puck robots, so that - over time - the robots learn how to flock. This demo is based on Paul's experimental work, which I described in some detail in an earlier blog post, so I won't go into detail here. It's the robots with the yellow hats in the lower picture above. What's interesting to observe is that initially, the robots are hopeless - constantly crashing into each other or the arena walls, but noticeably over 30 minutes or so we can see the robots learn to control themselves, using information from their sensors. The weird thing here is that, every minute or so, each robot's control software is replaced by a great-great-grand child of itself. The robot's body is not evolving, but invisibly it's controller is evolving, so that later generations of controller are more capable.
The magical moment of the two days was when one young lad - maybe 12 years old, who very clearly understood everything straight away and seemed to intuit things I hadn't explained - stayed nearly an hour explaining and demonstrating to other children. Priceless.
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