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.
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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
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DOI: https://doi.org/10.1007/978-3-319-08864-8_23
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