Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers

<p><strong>Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers</strong>, by Fernando Silva, Luís Correia, and Anders Lyhne Christensen.</p> <p>To appear in: EPIA 2013. 16th Portuguese Conference on Artificial Intelligence. Springer-Verlag.</p> <p> </p> <p><strong>Abstract: </strong>In this paper, we investigate the dynamics of different neuronal models on online neuroevolution of robotic controllers in multirobot systems. We compare the performance and robustness of neural network-based controllers using summing neurons, multiplicative neurons, and a combination of the two. We perform a series of simulation-based experiments in which a group of e-puck-like robots must perform an integrated navigation and obstacle avoidance task in environments of different complexity. We show that: (i) multiplicative controllers and hybrid controllers maintain stable performance levels across tasks of different complexity, (ii) summing controllers evolve diverse behaviours that vary qualitatively during task execution, and (iii) multiplicative controllers lead to less diverse and more static behaviours that are maintained despite environmental changes. Complementary, hybrid controllers exhibit both behavioural characteristics, and display superior generalisation capabilities in simple and complex tasks. </p> <p> </p> <p><strong>Description of the dataset:</strong></p> <p>The dataset contains a number of figures and .gv files that represent artificial neural networks used as evolved robotic controllers. There are also a number of .txt files describing each network as a set of connections. Each line in a given file describes a connection as a triple (input neuron, output neuron, connection weight).</p> <p> </p> <p>For a given experimental setup (see the paper), each controller evolved is named as "S.R", where S represents the sample/run in which the controller was evolved, and R is the id of the robot.</p> <p> </p> <p>Networks are described in the following manner:</p> <p>- Ri denotes sensor i for robot detection, with i in [1:8]</p> <p>- Wi denotes sensor i for wall/obstacle detection, with i in [1:8]</p> <p>- E denotes the virtual energy level sensor</p> <p>- Hx denotes the hidden neuron H with id x</p> <p>- LW represents the output neuron that controls the robot's left wheel</p> <p>- RW represents the output neuron that controls the robot's right wheel</p