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Evolution in Robotic Islands.

Evolution in Robotic Islands - Project Wiki.



This wiki is a repository for information and experiments pertaining to the Ariadna project Evolution in Robotic Islands - a joint collaboration between The Advanced Concepts Team at the European Space Agency, and The Centre for Robotics and Neural Systems at The University of Plymouth.

For more information on the the project, please visit: Evolution in robotic islands

Latest Experiments

To view the results from the latest experiments, please visit: Results

Study Participants

Project Poster Outlining Research

Centre for Robotics and Neural Systems, University of Plymouth

Advanced Concepts Team, European Space Agency



In order to meet the needs of experiments conducted during the study, the Advanced Concepts Team has developed a software platform freely available as an open source project named Parallel Global Multiobjective Optimiser (PaGMO). The code, entirely written in C++ and tested on Windows XP, Ubuntu, and Leopard OS, implements the asynchronous island model paradigm for generic optimisation purposes. It defines Individuals, Populations, Islands and Archipelagi as C++ objects, providing an easy-to-use computational environment where to simulate the concurrent evolution of populations. PaGMO objects can also be instantiated directly from Python and each island can be assigned the same or a different algorithm to evolve its population. The evolution in each island is assigned to a different thread of the underlying operating system so that parallelisation is obtained when multiple processors are available. Communication (migration) between threads (islands) occur in an asynchronous fashion. Such a generic framework facilitates studies on the island model impact on optimisation algorithms in general and population-based meta-heuristics in particular.

PaGMO Project Page on SourceForge

Mars Rover Simulator

The University of Plymouth has developed the Mars Rover Simulator project, based on the evolutionary robotics paradigm where an artificial agent acquires its skills through the process of artificial evolution. This simulator can be useful to evolve neural network controllers for the rover. The robot model is based on a 3D simulation model of the MSL rover.

Mars Rover Research - main page

Video of Initial Simulator Research

MarsRoverSim Project Page on SourceForge


M. Peniak, B. Bentley, D.Marocco, A. Cangelosi, C. Ampatzis, D. Izzo, F. Biscani (in press). An island-model framework for evolving neuro-controllers for planetary rover control. Proceedings of IJCNN2010 International Joint Conference on Neural Networks, Barcelona, July 2010.

M. Peniak, B. Bentley, D. Marocco, C. Ampatzis, F. Biscani, D. Izzo, A. Cangelosi (2010). Designing Autonomous Robot Controllers for Planetary Exploration: A Model of a Mars Rover. Postgraduate Conference for Computing: Applications and Theory (PCCAT). Exeter, United Kingdom, June - Awarded Best Paper link

C. Ampatzis, D. Izzo, M. Rucinski and F. Biscani (2009). ALife in the Galapagos: migration effects on neuro-controller design, Proceedings of the European Conference on Artificial Life (ECAL 2009). link.

M. Peniak, D.Marocco, A. Cangelosi (2009). Co-evolving controller and sensing abilities in a simulated Mars Rover explorer. IEEE Congress on Evolutionary Computation (CEC) 2009. Trondheim Norway, 18th-21nd May link.

M. Peniak, D. Marocco, S. Ramirez-Contla and A. Cangelosi (2009). An active vision system for navigating unknown environments: An evolutionary robotics approach for space research. In: Proceedings of IJCAI-09 Workshop on Artificial Intelligence in Space, Pasadena, California, 17-18 July 2009 link.

M. Peniak, A. Cangelosi, D.Marocco (2008). Autonomous robot exploration of unknown terrain: A preliminary model of Mars Rover robot. 10th ESA Workshop on Advanced Space Technologies for Robotics and Automation. Noordwijk, November link.

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