This thesis aims to transfer knowledge from insect biology into a hexapod walking robot. The similarity of the robot model to the biological target allows the testing of hypotheses regarding control and behavioural strategies in the insect. Therefore, this thesis supports biorobotic research by demonstrating that robotic implementations are improved by using biological strategies and these models can be used to understand biological systems. Specifically, this thesis addresses two central problems in hexapod walking control: the single leg control mechanism and its control variables; and the different roles of the front, middle and hind legs that allow a decentralised architecture to co-ordinate complex behavioural tasks. To investigate these problems, behavioural studies on insect curve walking were combined with quantitative simulations. Behavioural experiments were designed to explore the control of turns of freely walking stick insects, Carausius morosus, toward a visual target. A program for insect tracking and kinematic analysis of observed motion was developed. The results demonstrate that the front legs are responsible for most of the body trajectory. Nonetheless, to replicate insect walking behaviour it is necessary for all legs to contribute with specific roles. Additionally, statistics on leg stepping show that middle and hind legs continuously influence each other. This cannot be explained by previous models that heavily depend on positive feedback controllers. After careful analysis, it was found that the hind legs could actively rotate the body while the middle legs move to the inside of the curve, tangentially to the body axis. The single leg controller is known to be independent from other legs but still capable of mechanical synchronisation. To explain this behaviour positive feedback controllers have been proposed. This mechanism works for the closed kinematic chain problem, but has complications when implemented in a dynamic model. Furthermore, neurophysiological data indicate that legs always respond to disturbances as a negative feedback controller. Additional experimental data presented herein indicates that legs continuously oppose forces created by other legs. This thesis proposes a model that has a velocity positive feedback control modulated via a subordination variable in cascade with a position negative feedback mechanism as the core controller. This allows legs to oppose external and internal forces without compromising inter-leg collaboration for walking. The single leg controller is implemented using a distributed artificial neural network. This network was trained with a wider range of movement to that so far found in the simulation model. The controller implemented with a plausible biological.

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