Abstract
The landscape of isolated areas has been changed due to human intervention to support vehicular transport, however, this is a hectic job, therefore, if our vehicles are morphed to mimic nature, the landscape would not need to be changed. Robots and vehicles inspired from nature are very hard to control because of multiple number of actuators. Manual methods (such as programming individual actuators to form a walking pattern) fall short because of the complexity. Therefore, an automated process that employs artificial intelligence (AI) to evolve locomotion gaits for quadrupedal robots is needed. AI has been used before as well; however, most of the AI implementations are only done in simulation without hardware execution. This article attempts to use genetic algorithms to evolve locomotion gaits that are later implemented on robots both via simulations and real implementation. The simulation is run for 200 generations and the best result is put into effect on a hardware robot. Our results show that the gait is successfully transferred; however, the results are not perfect and suffer from the reality gap. These results also help us conclude that gaits designed for a specific environment have a better chance of transferring than gaits that have been designed without taking into account the surface the robot walks on.
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Mir, U., Khan, Z., Mir, U.I., Naseer, F., Shah, W. (2019). Evolution of Locomotion Gaits for Quadrupedal Robots and Reality Gap Characterization. In: Martín-Vide, C., Pond, G., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2019. Lecture Notes in Computer Science(), vol 11934. Springer, Cham. https://doi.org/10.1007/978-3-030-34500-6_14
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DOI: https://doi.org/10.1007/978-3-030-34500-6_14
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