Abstract
This paper focuses on generating and analyzing collective step-climbing behavior in a multi-legged robotic swarm. The multi-legged robotic swarm is expected to climb obstacles that are hard for a single robot by using other robots as stepping stones. However, designing a robot controller for a multi-legged robotic swarm becomes a challenging problem because it designs not only a gait for the basic movement of robots but also the behavior of robots to exhibit collective behavior. This paper employs the evolutionary robotics (ER) approach for designing a robot controller that consists of a recurrent neural network. The controllers are evaluated in the collective step-climbing task conducted by computer simulations. The results show that the ER approach successfully designed the robot gait to achieve the task. Additionally, the results of the analysis confirm that the robot obtained the actions to support other robots along with climbing other robots.
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This work was partially supported by the Hokkaido University Information Initiative Center and by JSPS KAKENHI Grant Number JP21J23095.
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Morimoto, D., Hiraga, M., Ohkura, K., Munetomo, M. (2022). Generating and Analyzing Collective Step-Climbing Behavior in a Multi-legged Robotic Swarm. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_29
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DOI: https://doi.org/10.1007/978-3-031-20176-9_29
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