Renzo De Nardi

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Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human(More)
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(More)
As the MAV (Micro or Miniature Aerial Vehicles) field matures, we expect to see that the platform's degree of autonomy, the information exchange, and the coordination with other manned and unmanned actors, will become at least as crucial as its aerodynamic design. The project described in this paper explores some aspects of a particularly exciting possible(More)
This chapter surveys the research of us and others into applying evolutionary algorithms and other forms of computational intelligence to various aspects of racing games. We first discuss the various roles of computational intelligence in games, and then go on to describe the evolution of different types of car controllers, modelling of players’ driving(More)
Examples of quadrotor helicopters models described in the literature (e.g. [6], [4], [17]) tend to focus on reproducing only the dynamic aspects the aerial platform and their primary use is in the domain of closed loop flight control. When the aim is simulating more general higher level tasks that involve multiple platforms which sense and react in their(More)
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(More)
The SUAAVE project is funded by EPSRC under the WINES wireless networking initiative to consider issues of multiple aerial vehicles communicating and collaborating in performing tasks, and involves teams from University College London, University of Ulster, and the University of Oxford. The focus of SUAAVE lies in the creation and control of swarms of UAVs(More)
The paper considers the problem of synthesising accurate dynamic models of a miniature rotorcraft based on minimal physical assumptions, and using the models to develop a controller. The approach is based on the idea of building models that predict accelerations, and is implemented using evolutionary programming in a particularly efficient co-evolutionary(More)
Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper(More)