Robust control of a humanoid robot using a bio-inspired approach based on central pattern generators, reflexes, and proprioceptive feedback

Bartsch, Sebastian; Kirchner, Frank

doi:10.1109/robio.2006.340174

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…5(a), the MLEs of CPGs' outputs are zero for b ∈ [3. 5,11] , [12, 18.5] , [19.5, 26.5] , suggesting that the states of CPGs' outputs are limit cycles. Otherwise, the MLEs are negative or positive, suggesting that the states of CPGs' outputs are stable or chaotic.…”

Section: Cpg Network Modelmentioning

confidence: 99%

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Coupling effect analysis between the central nervous system and the CPG network with proprioception

Wang

2014

Robotica

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Human rhythmic movement is generated by central pattern generators (CPGs), and their application to robot control has attracted interest of many scientists. But the coupling relationship between the central nervous system and the CPG network with external inputs is still not unveiled. According to biological experiment results, the CPG network is controlled by the neural system; in other words, the interaction between the central nervous system and the CPG network can control human movement effectively. This paper offers a complex human locomotion model, which illustrates the coupling relationship between the central nervous system and the CPG network with proprioception. Based on Matsuoka's CPG model (K. Matsuoka, Biol. Cybern. 52(6), 367-376 (1985)), the stability and robustness of the CPG network are analyzed with external inputs. In order to simulate the coupling relationship, the Radial Basis Function (RBF) neural network is used to simulate the cerebral cortex, and the Credit-Assignment Cerebellar Model Articulation Controller algorithm is employed to realize the locomotion mode conversion. A seven-link biped robot is chosen to simulate the walking gait. The main discoveries include: (1) the output of a new CPG network, which is stable and robust, can be treated as proprioception. Proprioception provides the central nervous system with the information about all joint angles; (2) analysis on a new locomotion model reveals that the cerebral cortex can modulate CPG parameters, leading to adjustment in walking gait.

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Section: Cpg Network Modelmentioning

confidence: 99%

“…central nervous system needs to know the angles of all joints between the hand and the body so as to combine these angles with known segment lengths to localize a hand. Bartsch et al 11 treated the CPG as a proprioception in the locomotion of a humanoid robot.…”

Section: Introductionmentioning

confidence: 99%

Coupling effect analysis between the central nervous system and the CPG network with proprioception

Wang

2014

Robotica

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“…The central nervous system needs to know the angles of all joints between the hand and the body so as to combine these angles with the known segment lengths to localize a hand. Bartsch and Kirchner (2006) treated the CPG as a proprioception in the locomotion of the humanoid robot. In this paper, the CPG is treated as the proprioception from the musculoskeletal system and is fed back to the basal ganglia.…”

Section: Introductionmentioning

confidence: 99%

Effects on hypothalamus when CPG is fed back to basal ganglia based on KIV model

Tian

et al. 2014

Cogn Neurodyn

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The KIV model approximates the operation of the basic vertebrate forebrain together with the basal ganglia and motor systems. In KIV model, the hypothalamus and the basal ganglia which are two important parts in the midline forebrain are closely associated with the locomotion. The CPG model with time delay is established in this paper and the stability of this CPG model is discussed. The CPG output is treated as the proprioception and fed back to the basal ganglia. We focus on the effects on the hypothalamus and the basal ganglia when the time delay parameter a d , the CPG amplitude parameter e and the CPG frequency parameter T r are changed. Through analysis, we find that there exists optimum value of the parameters a d or T r which can make the synchronization of the hypothalamus optimum when the CPG is added into the basal ganglia. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.

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“…In the search for better algorithms and approaches for achieving better locomotion and dynamics of humanoid robots, researchers, besides the classical mathematical modeling approaches, have also tried to use biologically inspired paradigms for this domain [19] - [22].…”

Section: Introductionmentioning

confidence: 99%

“…Some of them are based on spinal central pattern generators (CPGs) in vertebrate systems [19], others use the CPG in relation with modulation of stiffness [20], reflex based stabilization using SMA muscles [21] or coupled oscillators [22].…”

Section: Introductionmentioning

confidence: 99%

SelSta - A Biologically Inspired Approach for Self-Stabilizing Humanoid Robot Walking

Jakimovski

Kotke

Hörenz

et al. 2010

Distributed, Parallel and Biologically Inspired Systems

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Robust control of a humanoid robot using a bio-inspired approach based on central pattern generators, reflexes, and proprioceptive feedback

Cited by 4 publications

References 5 publications

Coupling effect analysis between the central nervous system and the CPG network with proprioception

Coupling effect analysis between the central nervous system and the CPG network with proprioception

Effects on hypothalamus when CPG is fed back to basal ganglia based on KIV model

SelSta - A Biologically Inspired Approach for Self-Stabilizing Humanoid Robot Walking

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