Abstract
The central pattern generator (CPG) has been found to be a real, existing neuron controller for the locomotion control of animals and it has been used on bio-inspired robots widely in recent years. However, research on the adaptability of CPG-based locomotion control methods is still a challenge. In particular, the performance of the CPG method on quadruped robots is not good enough in some situations compared with the traditional force control methods. In this article, we adopt a CPG method in which phase difference between oscillators can be arbitrarily adjusted, and we try to improve the CPG's applications in quadruped robots in some aspects. One aspect is static walk gait locomotion, in which we try to add a transition state in the CPG network to enhance the static balance of the robot. Another aspect is gait transition. Compared with the traditional abrupt gait transition, we try to realize a continuous gait transition between walk gait and trot gait to decrease the fluctuations of the robot. The improved CPG method is tested on a quadruped model and it shows positive results with regard to the improvement of static walk gait and gait transitions.
Highlights
Most species in the terrestrial world employ legged locomotion and can perform diverse behaviours
With the function of setting different ascending frequency and descending frequency, the proposed method of constructing central pattern generator (CPG) can be applied to many CPGs control applications
CPG is generated by coupled neurons whereby different coupling relationships represent different gaits
Summary
Most species in the terrestrial world employ legged locomotion and can perform diverse behaviours. D10H,o2n6g9w:2e0i 1L3i: 1 CPGs With Continuous Adjustment of Phase Difference for Locomotion Control making CPG‐based locomotion controllers suitable for legged robots. Ijspeert et al have made exciting progress in different aspects of CPG‐based locomotion control, including the CPG framework, feedback of sensor information, coupling with the mechanics and gait transitions, etc. If the COGs offset is long enough into the triangular stable region generated by the stance limbs, the robot will not lose balance when a hind limb lifts up. Smooth and gradual gait transitions can be realized with the special structure of our CPG models in which the phase differences between neurons can be continuously adjusted. Improvement of the trajectories of COG in a static walk gait is discussed; in addition, we illustrate how the gait transition process becomes smoother using our proposed CPG method.
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