Snakebot was invented by Dr Ivan Tanev (Doshisha University, Japan), and is simulated as a set of identical spherical morphological segments, linked together via universal joints. The morphology is inspired by the efficient side-winding locomotion of the rattlesnake Crotalus cerastes. Actuators (“muscles”) are attached to individual segments (“vertebrae”): in the initial standstill position of Snakebot, the rotation axes of the actuators are oriented vertically (vertical actuator) and horizontally (horizontal actuator). Open Dynamics Engine (ODE) was chosen to provide a realistic simulation of the mechanics of Snakebot. There is no global coordinator component in the system evolved with Genetic Programming techniques.
Locomotion of a simulated Snakebot that was evolved with information-driven fitness functions (in collaboration with Joseph Lizier, Dr Ivan Tanev and Dr Vadim Gerasimov; 2006-ongoing):
It can be shown that the amount of predictive information between groups of actuators (measured via generalised excess entropy) grows as the modular robot starts to move across the terrain. That is, the distributed actuators become more coupled when a coordinated side-winding locomotion is dominant. The increase of predictive information is indicative of self-organisation. Faced with obstacles, the robot temporarily loses the side-winding pattern: the modules become less organised, the strength of their coupling is decreased, and rather than exploiting the dominant pattern, the robot explores various alternatives. Such exploration temporarily decreases self-organisation (the predictive information within the system is reduced). When the obstacles are avoided, the modules “rediscover” the dominant side-winding pattern by themselves, recovering the previous level of predictive information and manifesting again the ability to self-organise without any global controller.
Presence of predictive information does not mean that there are explicit segment-to-segment communication channels. Rather, the channel is created by physical coupling: if the states of the remote segments are synchronised then some information has been indirectly transferred via stigmergy (due to the physical interactions among the segments, and with the terrain).
1. M. Prokopenko, V. Gerasimov, and I. Tanev. Measuring spatiotemporal coordination in a modular robotic system . In L. Rocha, L. Yaeger, M. Bedau, D. Floreano, R. Goldstone, and A. Vespignani, eds, Artificial Life X: Proceedings of The 10th International Conference on the Simulation and Synthesis of Living Systems, 185–191, Bloomington IN, USA, 2006.
2. M. Prokopenko, V. Gerasimov, and I. Tanev. Evolving Spatiotemporal Coordination in a Modular Robotic System , in Nolfi, S., Baldassarre, G., Calabretta R., Hallam, J. C. T., Marocco, D., Meyer J.-A., Miglino, O., and Parisi, D., eds. From Animals to Animats 9: 9th International Conference on the Simulation of Adaptive Behavior (SAB 2006), Rome, Italy, Springer, Lecture notes in computer science, vol. 4095, 558-569, 2006.
3. I. Tanev, T. Ray, and A. Buller. Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot. IEEE Transactions On Robotics, 21:632–645, 2005.
4. I. Tanev, Learning Mutation Strategies for Evolution and Adaptation of a Simulated Snakebot, Advances in Applied Self-organizing Systems, M.Prokopenko, ed., 105-126, Springer, 2007.
5. J. T. Lizier, M. Prokopenko, I. Tanev, and A. Y. Zomaya, Emergence of Glider-like Structures in a Modular Robotic System, In Proceedings of 11th International Conference on the Simulation and Synthesis of Living Systems (ALife XI), 2008 (to appear).