Title
Neural Control for Gait Generation and Adaptation of a Gecko Robot
Abstract
Geckos are highly adaptable creatures, able to scale a variety of slopes, including walls, and can change their gait depending on their environment. Roboticists have tried to implement this behaviour in gecko robots. So far, an open-loop controlled robot without a tail that uses only one specific gait can climb to a 50° slope. In this paper, we propose neural control that allows a gecko robot to climb to a 70° slope by generating different gaits for various slope angles. The control consists of three main components: a central pattern generator (CPG) for generating various rhythmic patterns, CPG post-processing for shaping the CPG signals, and a delay line for transmitting the shaped CPG signals to drive the legs of the gecko robot. The robot uses a body inclination sensor to provide sensory feedback for gait adaptation. When the incline is below 35°, the robot walks with a predefined fast trot gait. If the incline is increased, it will change its gait from the trot gait to an intermediate gait, followed by a slow wave gait, which is both the most stable and the slowest gait, for climbing the steepest slopes. Using this walking strategy, the robot can efficiently climb a variety of slopes using different gaits and can automatically adapt its gait to maximise speed while ensuring stability.
Year
DOI
Venue
2019
10.1109/ICAR46387.2019.8981580
2019 19th International Conference on Advanced Robotics (ICAR)
Keywords
Field
DocType
gecko robot,open-loop controlled robot,neural control,slope angles,central pattern generator,shaped CPG signals,gait adaptation,predefined fast trot gait,intermediate gait,slow wave gait,gait generation,geckos
Astronomy,Creatures,Computer vision,Gecko,Neural control,Gait,Artificial intelligence,Robot,Central pattern generator,Climbing,Climb,Physics
Conference
ISBN
Citations 
PageRank 
978-1-7281-2468-1
0
0.34
References 
Authors
1
7