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.

Evolutionary-Optimized Central Pattern Generator for Stable Modifiable Bipedal Walking

2014 ◽  
Vol 19 (4) ◽  
pp. 1374-1383 ◽  
Author(s):  
Chang-Soo Park ◽  
Young-Dae Hong ◽  
Jong-Hwan Kim
Keyword(s):  
Central Pattern Generator ◽  
Pattern Generator ◽  
Bipedal Walking

A NOVEL CPG WITH PROPRIOCEPTION AND ITS APPLICATION ON THE LOCOMOTION CONTROL OF QUADRUPED ROBOT

2009 ◽  
Vol 06 (01) ◽  
pp. 33-46 ◽  
Author(s):  
LEI SUN ◽  
MAX Q.-H. MENG ◽  
SHUAI LI ◽  
HUAWEI LIANG ◽  
TAO MEI
Keyword(s):  
Central Pattern Generator ◽  
Sensory Information ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Locomotion Control ◽  
Working Status ◽  
Self Protection ◽  
Dynamic Connectivity

This paper proposes a novel central pattern generator (CPG) model with proprioceptive mechanism and the dynamic connectivity mechanism. It not only contains the sensory information of the environment but also contains the information of the actuators and automatically tunes the parameters of CPG corresponding to the actuators information and inner sensory information. The position of the joints linked directly with the output of CPG is introduced to the CPG to find its proprioceptive system, spontaneously making the robot realize the actuator working status, further changing the CPG output to fit the change and decrease the influence of the problematic joints or actuators on the robot being controlled. So the damage would be avoided and self-protection is implemented. Its application on the locomotion control of a quadruped robot demonstrates the effectiveness of the proposed approach.

Walking quality guaranteed central pattern generator control method

2013 ◽  
Vol 228 (3) ◽  
pp. 569-579
Author(s):  
Jiaqi Zhang ◽  
Xiaolei Han ◽  
Xueying Han
Keyword(s):  
Central Pattern Generator ◽  
Control Method ◽  
Central Pattern Generators ◽  
Pattern Generator ◽  
Walking Robots ◽  
High Quality ◽  
Traditional Methods ◽  
Walking Gait ◽  
Pattern Generators ◽  
Periodic Inputs

Creating effective locomotion for a legged robot is a challenging task. Central pattern generators have been widely used to control robot locomotion. However, one significant disadvantage of the central pattern generator method is its inability to design high-quality walks because it only produces sine or quasi-sine signals for motor control as compared to most cases in which the expected control signals are more advanced. Control accuracy is therefore diminished when traditional methods are replaced by central pattern generators resulting in unaesthetically pleasing walking robots. In this paper, we present a set of solutions, based on testings of Sony’s four-legged robotic dog (AIBO), which produces the same walking quality as traditional methods. First, we designed a method based on both evolution and learning to optimize the walking gait. Second, a central pattern generator model was put forth to enabled AIBO to learn from arbitrary periodic inputs, which resulted in the replication of the optimized gait to ensure high-quality walking. Lastly, an accelerator sensor feedback was introduced so that AIBO could detect uphill and downhill terrains and change its gait according to the surrounding environment. Simulations were performed to verify this method.

Control of Locomotion in Crustaceans

2017 ◽  
pp. 470-494
Author(s):  
Daniel Cattaert ◽  
Donald Hine Edwards
Keyword(s):  
Neural Networks ◽  
Central Pattern Generator ◽  
Hybrid System ◽  
Sensory Feedback ◽  
Motor Command ◽  
Pattern Generator ◽  
Decapod Crustaceans ◽  
Leg Coordination ◽  
Leg Movements

This chapter will consider the control of posture and walking in decapod crustaceans (crabs, lobsters, rock lobsters, and crayfish). The walking system of crustaceans is composed of five pairs of appendages, each with seven articulated segments. While crabs sideways walking relies on stereotyped trailing and leading leg movements, forward/backward walking in lobsters and crayfish is achieved by different movements in the different legs, depending on their orientation versus body axis. Largely independent neural networks, localized in each of the 10 hemi-segmental thoracic ganglia, control each leg during locomotion. Each of these networks is modularly organized, with a specific central pattern generator (CPG) controlling each joint. Although coordinating interneurons have been described, inter-joint and inter-leg coordination is largely maintained by sensory feedback. Recently, the key role of proprioceptive signals in motor command processing has been addressed thanks to hybrid system experiments and modelling.

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