Abstract
Although quite a few central pattern generator controllers have been developed to regulate the locomotion of terrestrial bionic robots, few studies have been conducted on the central pattern generator control technique for amphibious robots crawling on complex terrains. The present article proposes a central pattern generator and feedforward neural network-based self-adaptive gait control method for a crab-like robot locomoting on complex terrain under two reflex mechanisms. In detail, two nonlinear ordinary differential equations are presented at first to model a Hopf oscillator with limit cycle effects. Having Hopf oscillators as the basic units, a central pattern generator system is proposed for the waveform-gait control of the crab-like robot. A tri-layer feedforward neural network is then constructed to establish the one-to-one mapping between the central pattern generator rhythmic signals and the joint angles. Based on the central pattern generator system and feedforward neural network, two reflex mechanisms are put forward to realize self-adaptive gait control on complex terrains. Finally, experiments with the crab-like robot are performed to verify the waveform-gait generation and transition performances and the self-adaptive locomotion capability on uneven ground.
Highlights
Central pattern generator (CPG) is a biological concept that represents the neural networks, which spontaneously generate the rhythmic movements for both invertebrate and vertebrate animals, such as breathing, chewing, swallowing, digesting, and walking.[1]
Because the rhythmic oscillation of CPGs depends on the internal information inside neurons and the interconnection information among neurons, the cyclic movements can be produced under the support of only very simple and nonrhythmic input signals or even without the help of sensory feedback.[2]
The biologically inspired CPG control method is presented for a crab-like robot with six legs
Summary
Central pattern generator (CPG) is a biological concept that represents the neural networks, which spontaneously generate the rhythmic movements for both invertebrate and vertebrate animals, such as breathing, chewing, swallowing, digesting, and walking.[1]. Biological CPGs are a system of distributed neural networks They can provide distributed control for rhythmic activities and gait transitions such as the switches from walk to trot and to gallop.[3,4] Four types of main biological CPG models have been proposed in the past years,[2] including the coupled nonlinear oscillator models,[5] the neuron-based biophysical models,[6] the simplified neuron-based connectionist models,[7] and the neuromechanical models.[8]. Most locomotion of robots is rhythmic, the concept of CPG has been extended into the field of robotics to construct artificial neural networks that produce the biologically inspired rhythmic gaits of the robotic body, legs, and/or arms.[3,4] Artificial CPGs, as a type of new technology, are more robust than the conventional model-based and behavior-based methods in robotic locomotion controlling, because CPGs always have the limit cycle behavior, which can rapidly generate stable rhythmic patterns even after a transient perturbation.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
