Paper Info
Title
Climbing obstacles via bio-inspired CNN-CPG and adaptive attitude control
Abstract
In this paper a control system based on the prin- ciples 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 playing the role of an artificial Central Pattern Generator 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. I. INTRODUCTION Explorative missions, e.g. to deliver a probe on a planetary surface or to inspect mined ground, represent a huge techno- logical challenge. Major issues to be addressed are: rover locomotion: rover should transverse uneven terrains with large obstacles; rover autonomy: rover for explorative mission should maneuver on harsh terrains and unknown environments without man control. Biology provides a wealth of inspiration: insects are able to transverse harsh terrains, to climb over obstacles or even to walk upside down. Moreover, essential aspects in unmanned missions as reconfigurability of locomotion strategies, naviga- tion capabilities and robustness are common features between insects. Therefore, several efforts, both from a behavioral viewpoint and from an architectural viewpoint, have been performed to design an insect-like robot. In this paper, we propose a new approach to the control of obstacle climbing in hexapod running robots totally based on exhaustive kinematic data reported in (5). Locomotion control is performed by a Central Pattern Generator implemented via Cellular Neural Network working in parallel with an attitude PID controller, whose references are provided adaptively by a Motor Map. On one hand the CPG provides the basic rhythmic signals needed for locomotion, on the other hand the Motor Map Controller (MMC) represents the higher level control that, basing on sensory feedback, allows the robot to climb over obstacles. Most of the researches on locomotion control in insects reveal the presence of a hierarchical organized neural sys- tem. Most of control schemes for legged robots also use a hierarchical organization. The main focus of this work is on the higher-order level providing adaptive capabilities to the robot control system, while the low level (locomotion control)
Year
DOI
Venue
2005
10.1109/ISCAS.2005.1465810
ISCAS (5)
Keywords
Field
DocType
adaptive control,attitude control,cellular neural nets,legged locomotion,mechanoception,three-term control,CNN,CPG,adaptive attitude control,artificial central pattern generator,attitude PID controller,bio-inspired CNN-CPG,cellular neural network,cockroaches,exteroceptive information,hexapod walking robots,insect-like robot,locomotion control,motor map,obstacle climbing,proprioceptive information,robot locomotion gait,robot posture
Obstacle,Robot control,Control theory,Computer science,Robot kinematics,Control engineering,Attitude control,Control system,Adaptive control,Robot,Hexapod
Conference
ISSN
ISBN
Citations 
0271-4302
0-7803-8834-8
3
PageRank 
References 
Authors
0.52
4
4
Name
Order
Citations
PageRank
Paolo Arena126147.43
Luigi Fortuna2761128.37
M. Frasca3468.05
Patane, L.4101.22