Continuous Modulation of Step Height and Length in Bipedal Walking, Combining Reflexes and a Central Pattern Generator

2018 ◽  
Author(s):  
Philippe Greiner ◽  
Nicolas Van Der Noot ◽  
Auke Jan Ljspeert ◽  
Renaud Rousse
Keyword(s):  
Central Pattern Generator ◽  
Pattern Generator ◽  
Step Height ◽  
Bipedal Walking

scholarly journals Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking

2021 ◽  
Vol 15 ◽  
Author(s):  
Dai Owaki ◽  
Shun-ya Horikiri ◽  
Jun Nishii ◽  
Akio Ishiguro
Keyword(s):  
Central Pattern Generator ◽  
Environmental Changes ◽  
Sensory Information ◽  
Motor Command ◽  
Pattern Generator ◽  
Bipedal Walking ◽  
Walking Robots ◽  
Two Dimensional ◽  
Limb Coordination ◽  
Systematic Methodology

Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for designing a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended motor command, in designing localised controllers in the CPG-based bipedal walking model. To this end, we developed a Tegotae function that quantifies the Tegotae concept. This function allowed incorporating decentralised controllers into the proposed bipedal walking model systematically. We designed a two-dimensional bipedal walking model using Tegotae functions and subsequently implemented it in simulations to validate the proposed design scheme. We found that our model can walk on both flat and uneven terrains and confirmed that the application of the Tegotae functions in all joint controllers results in excellent adaptability to environmental changes.

scholarly journals Optimization of Central Pattern Generator-Based Torque-Stiffness-Controlled Dynamic Bipedal Walking

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
William Suliman ◽  
Chadi Albitar ◽  
Lama Hassan
Keyword(s):  
Central Pattern Generator ◽  
Joint Stiffness ◽  
Biped Robot ◽  
Neural Model ◽  
Pattern Generator ◽  
Joint Torque ◽  
Performance Criteria ◽  
Bipedal Walking ◽  
Particle Swarm Algorithm ◽  
Input Control

In this paper, we propose a central pattern generator-based model to control the walking motion of a biped robot. The model independently controls the joint torque and joint stiffness in real time. Instead of the phase-dependent neural model used by Huang in 2014, we adopt the same structure for all the walking phases, reducing the number of connections between neurons. This reduction enables the employment of the particle swarm algorithm to find the optimal values of these parameters which lead to different solutions with different performance criteria. The simulation of the proposed method on a seven-link bipedal walking model gave a good performance in the range of walking speeds, which is referred to as versatility, and in walking pattern transition. The achieved walking gaits are 1-period cyclic motions for all the input control signals except for few gaits. Besides, these 1-period cyclic motions have a good local and global stability. Finally, we expanded our neural model by adding connections that work only when the robot walks on uneven terrains, which improved the robot’s performance against this kind of perturbation.

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