AI Article Synopsis
- * While CPGs are commonly modeled as interconnected nonlinear systems, the role of feedback in these systems is often overlooked, despite its importance for locomotion.
- * This paper introduces a CPG for a hexapod robot using Cellular Neural Networks, incorporating feedback through motor-neuron dynamics, and presents experiments to validate this feedback-enhanced approach.
Article Abstract
Central Pattern Generators (CPGs) are a suitable paradigm to solve the problem of locomotion control in walking robots. CPGs are able to generate feed-forward signals to achieve a proper coordination among the robot legs. In literature they are often modelled as networks of coupled nonlinear systems. However the topic of feedback in these systems is rarely addressed. On the other hand feedback is essential for locomotion. In this paper the CPG for a hexapod robot is implemented through Cellular Neural Networks (CNNs). Feedback is included in the CPG controller by exploiting the dynamic properties of the CPG motor-neurons, such as synchronization issue and local bifurcations. These universal paradigms provide the essential issues to include sensory feedback in CPG architectures based on coupled nonlinear systems. Experiments on a dynamic model of a hexapod robot are presented to validate the approach introduced.
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| http://dx.doi.org/10.1142/S0129065703001698 | DOI Listing |
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