Abstract
In future space missions, versatile, robust, autonomous and adaptive robotic systems will be required to perform complex tasks. This can be realized using modular robots with the ability to reconfigure to various structures, which allows them to adapt to the environment as well as to a given task. As it is not possible to program beforehand the robots to cope with every possible situation, they will have to adapt autonomously. In this paper, we introduce a novel framework which allows modular robots to adapt physically (i.e., to change the structure) as well as internally (i.e. to learn the behavior) to achieve high-level tasks (e.g. ’climb-up the cliff’). The framework utilizes evolutionary methods for structure adaptation as well as to find a suitable behavior. The main idea of the framework is the utilization of simple motion skills combined by a motion planner to achieve the high-level task. This allows to achieve complex task easily without need to optimize complex behaviors of the robot.
The work in this paper was supported by MSMT grant No. 7AMV14DE007.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bihlmaier, A., Winkler, L., Wörn, H.: Automated Planning as a New Approach for the Self-Reconfiguration of Mobile Modular Robots. In: Robot Motion and Control (RoMoCo) (2013)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys (CSUR) 35(3), 268–308 (2003)
Černý, J., Kubalík., J.: Co-evolutionary Approach to Design of Robotic Gait. In: Applications of Evolutionary Computation (2013)
Cui, Z.H., Zeng, J.C., Sun, G.J.: A fast particle swarm optimization. International Journal of Innovative Computing, Information and Control 2(6), 1365–1380 (2006)
Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21(4), 642–653 (2008)
Liedke, J., Matthias, R., Winkler, L., Wörn, H.: The Collective Self-Reconfigurable Modular Organism (CoSMO). In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2013) (2013)
Landis, G.A.: Robots and humans: synergy in planetary exploration. Acta Astronautica 55(12), 985–990 (2004)
Winkler, L., Kettler, A., Szymanski, M., Wörn, H.: The Robot Formation Language - A Formal Descriptions of Formations for Collective Robots. In: Proceedings of IEEE Symposium on Swarm Intelligence 2011 (SIS 2011), pp. 102–109 (2011)
Winkler, L., Wörn, H., Friebel, A.: A Distance and Diversity Measure for Improving the Evolutionary Process of Modular Robot Organisms. In: Proceedings of IEEE Int. Conf. on Robotics and Biomimetics, ROBIO (2011)
Winkler, L., Neumann, S., Wörn, H.: A Framework for the Automatic Generation of Self-Reconfigurable Robot Organisms and the Optimization of the Gait for these Organisms. In: Proceedings of the IEEE Conference on Control, Systems & Industrial Informatics(ICCSII 2013) (2013)
Winkler, L., Vonasek, V., Wörn, H., Preucil, L.: Robot3D - A Simulator for Mobile Modular Self-Reconfigurable Robots. In: IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (2012)
Marbach, D., Ijspeert, A.J.: Online optimization of modular robot locomotion. In: Proceedings of the IEEE International Conference on Mechatronics and Automation, ICMA (2005)
Moubarak, P., Ben-Tzvi, P.: Modular and reconfigurable mobile robotics. Robotics and Autonomous Systems 60(12), 1648–1663 (2012)
Pouya, S., Aydin, E., Möckel, R., Ijspeert, A.J.: Locomotion gait optimization for modular robots; coevolving morphology and control. Procedia Computer Science 7 (2011)
Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22. ACM (1994)
Vonásek, V., Saska, M., Košnar, K., Přeučil, L.: Global motion planning for modular robots with local motion primitives. In: ICRA (2013)
Vonásek, V., Winkler, L., Liedke, J., Saska, M., Košnar, K., Přeučil, L.: Fast on-board motion planning for modular robots. In: ICRA (accepted, 2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Vonásek, V., Neumann, S., Winkler, L., Košnar, K., Wörn, H., Přeučil, L. (2014). Task-Driven Evolution of Modular Self-reconfigurable Robots. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-08864-8_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08863-1
Online ISBN: 978-3-319-08864-8
eBook Packages: Computer ScienceComputer Science (R0)