Abstract
To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals for each joint of the gecko-like robot. Subsequently, the gecko-like robot successfully walked, crossed obstacles and climbed steps in the vertical plane, based on stable rhythm control signals. Both simulations and experiments validating the feasibility of the proposed CPG motion control model are presented.
Highlights
Geckos are known for their excellent ability to overcome obstacles, climb walls and run on ceilings [1]
The motion control methods of a legged robot are usually based on its model, its behavior, or the methods of its central pattern-generator [12,13,14,15,16]
In this study,Analysis a motion control network model based on a central neural pattern gener4.3
Summary
Geckos are known for their excellent ability to overcome obstacles, climb walls and run on ceilings [1]. Gecko-like robots, which can move and work on a vertical wall, are popular and used worldwide [2,3], and can be employed for various tasks, such as antiterrorism activities, post-disaster rescue, engineering tests, and maintenance and inspection in hazardous environments [4,5,6,7]; the locomotion control of gecko-like robots is a challenging project, and the performance of gecko-like robots is still not good enough to meet most requirements. It can coordinate multiple degrees of freedom to generate multimodal motion by adjusting a few parameters, and it offers good self-stability and adaptability
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