Abstract
In this article, a modular neurocontroller is presented. It has the capability to generate a reactive behavior of walking machines. The neurocontroller is formed on the basis of a modular structure. It consists of the three different functionality modules: neural preprocessing, a neural oscillator network and velocity regulating networks. Neural preprocessing is for sensory signal processing. The neural oscillator network, based on a central pattern generator, generates the rhythmic movement for basic locomotion of the walking machines while the velocity regulating networks change the walking directions of the machines with respect to the sensory inputs. As a result, this neurocontroller enables the machines to explore in- and out-door environments by avoiding obstacles and escaping from corners or deadlock situations. It was firstly developed and tested on a physical simulation environment, and then was successfully transferred to the six-legged walking machine AMOS-WD06.
Highlights
The idea behind this article is to investigate the neural mechanisms controlling biologically inspired walking machines represented as sensor-driven systems
The biologically inspired six-legged walking machine AMOS-WD061 is employed as an experimental device for the development and testing of a neurocontroller causing a sensor-driven reactive behavior
This neurocontroller is created on the basis of a modular structure; i.e. it is flexible to adapt for controlling in different walking machines [19] and it is even able to modify for generating different reactive behaviors, e.g. sound tropism [20]
Summary
The idea behind this article is to investigate the neural mechanisms controlling biologically inspired walking machines represented as sensor-driven systems. The biologically inspired six-legged walking machine AMOS-WD061 is employed as an experimental device for the development and testing of a neurocontroller causing a sensor-driven reactive behavior. This neurocontroller is created on the basis of a modular structure; i.e. it is flexible to adapt for controlling in different walking machines [19] and it is even able to modify for generating different reactive behaviors, e.g. sound tropism (positive tropism) [20]. Conclusions and an outlook on future research are given in the last section
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