Robustness of a distributed neural network controller for locomotion in a hexapod robot

The robustness of a distributed neural-network controller for locomotion based on insect neurobiology has been used to control a hexapod robot. The robustness of the controller is investigated experimentally. Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.<
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