Motivated by the risks and costs associated with outdoor experiments, this paper presents a new multi-platform quadrotor simulator. The simulator implements a novel second-order dynamic model for a quadrotor, produced through evolutionary programming, and explained by domain knowledge. The model captures the effects of mechanics, aerodynamics, wind and rotational stabilization control on the flight platform. In addition, the simulator implements military-grade models for wind and turbulence, as well as noise models for satellite navigation, barometric altitude and orientation. The usefulness of the simulator is shown qualitatively by a comparing how coloured and white position noise affect the performance of offline, range-only SLAM. The simulator is intended to be used for planning experiments, or for stress-testing application performance over a wide range of operating conditions.

Geometric particle swarm optimization (GPSO) is a recently introduced formal generalization of traditional particle swarm optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In this paper we apply GPSO to the space of genetic programs represented as expression trees, uniting the paradigms of genetic programming and particle swarm optimization. The result is a particle swarm flying through the space of genetic programs. We present initial experimental results for our new algorithm.

The problem of how to acquire a model of a physical robot,which is fit for evolution of controllers that can subsequently be used to control that robot, is considered in the context of racing a radio-controlled toy car around a randomised track. Several modelling techniques are compared, and the specific properties of the acquired models that influence the quality of the evolved controller are discussed. As we aim tominimise the amount of domain knowledge used, we furtherinvestigate the relation between the assumptions about the modelled system made by particular modelling techniques and the suitability of the acquired models as bases for controller evolution. We find that none of the models acquired is good enough on its own, and that a key to evolving robustbehaviour is to evaluate controllers simultaneously on multiple models during evolution. Examples of successfully evolved racing control for the physical car are analysed.

The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so.

This paper explores the idea that it may be possible to combine two ideas - UAV flocking, and wireless cluster computing - in a single system, the UltraSwarm. The possible advantages of such a system are considered, and solutions to some of the technical problems are identified. Initial work on constructing such a system based around miniature electric helicopters is described.