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Learning outcomes were measured by a pictorial locomotion pattern classification test.
Two studies investigated the effectiveness of dynamic and static visualizations for a perceptual learning task (locomotion pattern classification).
Simulation experiments demonstrate that the proposed system is effective and robust in simultaneously constructing the adaptive structure and locomotion pattern.
In the previous section, we have mentioned that the ePaddle-EGM is capable of altering the locomotion pattern smoothly and instantaneously.
This non-reciprocating strategy can also be applied in the ePaddle-based hexapod robot to realize the fast transition of locomotion pattern.
At present, since the rotary of each active joint is unidirectional and continuous, it means the ePaddle-EGM is capable of altering the locomotion pattern smoothly and instantaneously.
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In other word, the switch between different locomotion patterns can be realized immediately.
Using this model, three major locomotion patterns of Anguilliform fish, including forward locomotion, backward locomotion and turning locomotion are investigated.
Several locomotion patterns in various structures were designed systematically using a CPG controller model and GA optimization.
Accordingly, for learning about locomotion patterns, dynamic visualizations are better suited than most, but not all static visualization formats.
Therefore, for mobile robots, in consideration of safety of system, the adjustment time between two different locomotion patterns is necessary and inevitable.
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