Embodied evolutionary (EE) algorithm for open-ended continuous adaptation of swarm robotics.
This uses the 'SituSim' (SITuated and embodied robotics SIMulator) written in Python (Dept. Eng. & Inf., University of Sussex).
The key files written by myself are HungryRobot_probabilistic2.py (found in situsim extensions folder) and MyForager.py (found in lab 5 folder).
All plots are also contained in the folder lab 5.
In the natural world, the evolutionary process provides organisms with adaptive capabilities in a completely decentralised and distributed manner; one in which the evaluation of genes is an implicit and embodied process directly affected by agents’ behaviour. This is in contrast to the majority of artificial evolutionary algorithms developed for the purpose of automated design and optimisation which rely on centralised mechanisms of control.
In this paper, we present an embodied evolutionary (EE) algorithm as a mechanism for the adaptive control of swarm robotics. Inspired by previous work by Watson et al. (2002), EE is presented as a cheap and effective method by which adaptation in a collective robotic system can be decentralised and autonomous, with candidate solutions evaluated in parallel. We take the work of Watson et al. (2002) further by testing the limits of the system’s adaptive abilities and viewing its performance through the lens of W. Ross Ashby’s early work on ultrastable systems, and exploring the potential for open-ended continuous adaptation.
We find that our system can adapt to a range of environmental conditions and is moderately successful in its ability to adapt to large-scale perturbations. Our analyses provide us with preliminary insight into which parameters of the algorithm which determine its success and have pointed us towards opportunities for future research in this area.