Climbing Obstacle in Bio-robots via CNN and Adaptive Attitude Control

In this paper, we introduce a novel control system architecture for hexapod robots. Our aim is to guarantee efficient horizontal walking and obstacle climbing via suitable postural adjustments. The control scheme takes its inspiration from recent neurobiological and kinematic observations of cockroaches walking on a treadmill and climbing over barriers. Based on a hierarchical and modular approach, the control architecture is divided into two levels. In the low level two parts working in parallel are present: rhythmic movements leading to gaits are performed by a cellular neural network (CNN) playing the role of an artificial central pattern generator (CPG), while a parallel PD attitude control system modulates (with adding terms) the CNN-CPG signals to achieve postural adjustments. The higher level, in turn, adds plasticity to the whole system; it is based on motor maps and maps sensory information in suitable attitude references for the low level PD attitude control. Tests performed with a dynamic model of hexapod have shown that after a training period the high level is able to enhance walking and climbing capabilities. Copyright (c) 2006 John Wiley & Sons, Ltd.