Space Robotics Research

European Space Agency - Ariadna Project:
Evolution in Robotic Islands

Advanced Concepts Team, ESA

Christos Ampatzis
Dario Izzo
Francesco Biscani

Centre for Robotics & Neural Systems

Angelo Cangelosi
Davide Marrocco
Martin Peniak
Barry Bentley

Official Study Description

Evolutionary Robotics is a technique which has recently received growing attention from the robotics research community, as it promises the automatic synthesis of robot controllers. Such an automated framework is based on the use of artificial evolution to reinforce learning within robot populations, by effectively tuning the parameters of randomly generated controllers, where the controllers are typically artificial neural networks (ANNs).

The parallelisation of artificial evolution has been extensively studied in the context of global optimisation, and has been shown to significant speed up the optimisation process. In addition, island-based simulations have demonstrated that the migration of individuals between independent evolutionary runs improves the performance of the optimisation process, both in terms of function evaluations required, and in terms of the quality of the solutions obtained, providing a better balance between exploration and exploitation of the search space. This technique has also been applied to high dimensional and difficult engineering problems.

The island evolution paradigm has potential in the design of automated space robots for various reasons:
  • It could significantly speed up the design process by exploiting parallelism, while improving the quality of the solutions found, relative to traditional evolutionary techniques.
  • It could relieve the experimenter from a significant part of the burden required to properly set up the evolutionary process. For example, poorly-tuned algorithms cooperating via migration have been shown to work as well as a single instance of a well-tuned algorithm in global optimisation.
  • When designing controllers for robots, the exchange of individuals between islands, corresponds to the introduction of new solutions. This increases the diversity in a population, and the via recombination of genetic material might endow agents with capabilities that would be extremely unlikely to evolve in a single run; in other words, it could facilitate the progressive composition of a rich behavioural repertoire.

The main objective of this study is to use island evolution to optimise the neuro-controller of a model Mars rover, with a vision to empirically demonstrate an improvement in automated design.


This study was completed in 2010. Results can be found in the listed publications.


  • Peniak M., Bentley B., Marocco D., Cangelosi A., Ampatzis C., Izzo D. & Biscani F. (2010) An Evolutionary Approach to Designing Autonomous Planetary Rovers. Proceedings of TAROS (Towards Autonomous Robotic Systems), August 31 - September 2, 2010, Plymouth, UK. (pdf)
  • Peniak M., Bentley B., Marocco D., Cangelosi A., Ampatzis C., Izzo D. & Biscani F. (2010) An Island-Model Framework for Evolving Neuro-Controllers for Planetary Rover Control. Proceedings of IEEE International Joint Conference on Neural Networks, July 18 - 23, 2010, Barcelona, Spain. (pdf)
  • Cangelosi A., Marocco D., Peniak M., Bentley B., Ampatzis C. & Izzo D. (2010) Evolution in robotic islands - optimising the design of autonomous robot controllers for navigation and exploration of unknown environments. Advanced Concepts Team Report, The European Space Agency (ESA); Noordwijk, The Netherlands. Ariadna-ID: 09-8301 (pdf)
  • Peniak M., Bentley B., Marocco D., Ampatzis C., Biscani F., Izzo D. & Cangelosi A. (2010) Designing Autonomous Robot Controllers for Planetary Exploration: A Model of a Mars Rover. Proceedings of PCCAT (Postgraduate Conference for Computing: Applications and Theory), June 9, 2010, Exeter, UK. (pdf) - Awarded Best Paper

Presented at TAROS 2010, August 31 - September 2, 2010, Plymouth, UK

Project Poster

Ariadna project meeting at the
European Space Research and Technology Centre (ESTEC)
- July 2010