A cooperative active perception approach for swarm robotics - Romano, Pedro Sousa (2019)
More than half a century after modern robotics first emerged, we still face a landscape in which most of the work done by robots is predetermined, rather than autonomous. A strong understanding of the environment is one of the key factors for autonomy, enabling the robots to make correct decisions based on the environment surrounding them. Classic methods for obtaining robotic controllers are based on manual specification, but become less trivial as the complexity scales. Artificial intelligence methods like evolutionary algorithms were introduced to synthesize robotic controllers by optimizing an artificial neural network to a given fitness function that measures the robots’ performance to solve a predetermined task. In this work, a novel approach to swarm robotics environment perception is studied, with a behavior model based on the cooperative identification of objects that fly around an environment, followed by an action based on the result of the identification process. Controllers are obtained via evolutionary methods. Results show a controller with a high identification and correct decision rates. The work is followed by a study on scaling up that approach to multiple environments. Experiments are done on terrain, marine and aerial environments, as well as on ideal, noisy and hybrid scenarios. In the hybrid scenario, different evolution samples are done in different environments. Results show the way these controllers are able to adapt to each scenario and conclude a hybrid evolution is the best fit to generate a more robust and environment independent controller to solve our task.