Training of robotic swarms is usually done for a specific task and environment. The more specific the training is, the more the likelihood of reaching a good performance. Still, flexibility and robustness are essential for autonomy, enabling the robots to adapt to different environments. In this work, we study and compare approaches to robust training of a small simulated swarm on a task of cooperative identification of moving objects. Controllers are obtained via evolutionary methods. The main contribution is the test of the effectiveness of training in multiple environments: simplified versions of terrain, marine and aerial environments, as well as on ideal, noisy and hybrid (mixed environment) scenarios. Results show that controllers can be generated for each of these scenarios, but, contrary to expectations, hybrid evolution and noisy training do not, in general, generate better controllers for the different scenarios. Nevertheless, the hybrid controller reaches a performance level par with specialized controllers in several scenarios, and can be considered a more robust solution.

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Evolutionary robotics,Multirobot systems,Cooperation,Perception,Object identification,Artificial intelligence