An Investigation of Loose Coupling in Evolutionary Swarm Robotics

In complex systems, it has been observed that the parts within the system are "loosely coupled". Loose coupling means that the parts of the system interact in some way, and as long as this interaction is maintained the parts can evolve independently. Detrimental evolutionary changes within one part of the system do not negatively affect other parts. Overall system functionality is maintained, leading to faster evolution. In swarm robotic systems there are multiple robots working together to achieve a shared goal. However it is not always obvious how to program the actions of the robots such that the desired aggregate behaviour emerges. One solution is to use a genetic algorithm to evolve robot controllers, this approach is called "Evolutionary Swarm Robotics". This thesis makes the case that swarm robotic systems are complex systems, and hypothesises that loose coupling between the robots in a swarm would lead to faster evolution. Robot swarms are investigated where robots describe environmental features to each other as part of a foraging task. Multiple descriptions can be used to describe a feature. The mappings between feature descriptions, and the signals used to express those descriptions, are manipulated. By doing this, the interactions between robots can change over time or stay the same. Results show that loose coupling leads to higher swarmfitnesses because it makes the communicated information easier to interpret. However there are some subtleties in its working. We also observe that if some of the information is not useful for completing the task, this negatively affects swarm fitness regardless of coupling. This problem can be mitigated by using loose coupling. This research has implications for the design of communication within robot swarms. Before evolution, it is difficult to know what information is relevant. This research shows that sharing unnecessary information between robots is detrimental to swarm fitness because the cost of interpreting information can be greater than the benefit gained from the information. Loose coupling can reduce, but not eliminate, the evolutionary cost of interpreting multiple pieces of information in exchange for slower message transmission.