Abstract
Locomotion control in legged robots is an interesting research field that can take inspiration from biology to design innovative bio-inspired control systems. Central Pattern Generators (CPGs) are well known neural structures devoted to generate activation signals to allow a coordinated movement in living beings. Looking in particular in the insect world, and taking as a source of inspiration the Drosophila melanogaster, a hierarchical architecture mainly based on the paradigm of a Cellular non-linear Network (CNN) has been developed and applied to control locomotion in a fruit fly-inspired simulated hexapod robot. The modeled neural structure is able to show different locomotion gaits depending on the phase locking among the neurons responsible for the motor activities at the level of the leg joints and theoretical considerations about the generated pattern stability are discussed. Moreover the phase synchronization between the leg, altering the locomotion, can be used to modify the speed of the robot that can be controlled to follow a reference speed signal. To find the suitable transitions among patterns of coordinated movements, a reward-based learning process has been considered. Simulation results obtained in a dynamical environment using a Drosophila-inspired hexapod robot are here reported analyzing the performance of the system.
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This work was supported by EU Project EMICAB, grant no. 270182.
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Arena, E., Arena, P., Patané, L. (2015). Speed Control on a Hexapodal Robot Driven by a CNN-CPG Structure. In: Sirakoulis, G., Adamatzky, A. (eds) Robots and Lattice Automata. Emergence, Complexity and Computation, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-10924-4_5
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DOI: https://doi.org/10.1007/978-3-319-10924-4_5
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