In this paper a control system based on the principles used by cockroaches to climb obstacles is introduced and applied to a bio-inspired hexapod robot. Cockroaches adaptively use different strategies as functions of the ground morphology and obstacle characteristics. The control system introduced in this paper consists of two parts working in parallel. Locomotion control is performed by a Cellular Neural Network (CNN) playing the role of an artificial Central Pattern Generator (CPG) for the robot, while a new attitude control system has been designed. In order to reproduce the adaptative capabilities of the biological model, the attitude control system is based on a Motor Map and is aimed to regulate the posture of the robot to allow it to overcome obstacles. In fact high obstacles require the locomotion gait to be reorganized by changing the posture of the robot to be more effective during the overcoming of the obstacle. Both proprioceptive and exteroceptive information are needed to solve this problem, they constitute the input of the adaptive attitude control. Simulation results illustrating the suitability of the control system are also shown. © 2005 IEEE.
Climbing obstacles via bio-inspired CNN-CPG and adaptive attitude control
Patane L.;
2005-01-01
Abstract
In this paper a control system based on the principles used by cockroaches to climb obstacles is introduced and applied to a bio-inspired hexapod robot. Cockroaches adaptively use different strategies as functions of the ground morphology and obstacle characteristics. The control system introduced in this paper consists of two parts working in parallel. Locomotion control is performed by a Cellular Neural Network (CNN) playing the role of an artificial Central Pattern Generator (CPG) for the robot, while a new attitude control system has been designed. In order to reproduce the adaptative capabilities of the biological model, the attitude control system is based on a Motor Map and is aimed to regulate the posture of the robot to allow it to overcome obstacles. In fact high obstacles require the locomotion gait to be reorganized by changing the posture of the robot to be more effective during the overcoming of the obstacle. Both proprioceptive and exteroceptive information are needed to solve this problem, they constitute the input of the adaptive attitude control. Simulation results illustrating the suitability of the control system are also shown. © 2005 IEEE.Pubblicazioni consigliate
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