Locomotion Control of a Biped Robot through a Feedback CPG Network

Cristiano, Julián; Puig, Domènec; García, Miguel Ángel

doi:10.1007/978-3-319-03413-3_39

Cited by 7 publications

(14 citation statements)

References 11 publications

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“…In particular, the proposed control scheme has been implemented in the CPG network previously proposed by [31] for the locomotion control of a NAO humanoid robot. Fig.…”

Section: Application To Bipedal Locomotionmentioning

confidence: 99%

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Deterministic phase resetting with predefined response time for CPG networks based on Matsuoka’s oscillator

Cristiano

García

Puig

2015

Robotics and Autonomous Systems

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“…In particular, the proposed control scheme has been implemented in the CPG network previously proposed by [31] for the locomotion control of a NAO humanoid robot. Fig.…”

Section: Application To Bipedal Locomotionmentioning

confidence: 99%

“…Fig. 19 shows the schematic representation of that CPG network, whose parameters are described in [31]. This CPG net- work consists of a master Mastuoka's oscillator that drives a subset of slave Mastuoka's oscillators whose outputs control the angular displacement of the robot's joints.…”

Section: Application To Bipedal Locomotionmentioning

confidence: 99%

Deterministic phase resetting with predefined response time for CPG networks based on Matsuoka’s oscillator

Cristiano

García

Puig

2015

Robotics and Autonomous Systems

Self Cite

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“…Ishiguro et al [9] used a network of Matsuoka oscillators to control a 3D bipedal robot in simulation. An artificial neuromodulation mechanism was used for modulating the CPGs and the parameters were set [11]). Apart from CPG-1 (pacemaker oscillator), each CPG controls a single joint.…”

Section: Related Workmentioning

confidence: 99%

“…Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works In order to improve the adaptability and performance of a CPG-based bipedal controller, we propose a novel hierarchical architecture, in which a trainable high-level controller is added on top of a CPG network. The lower level of the controller, consisting of a CPG network (based on [11]) and its feedback pathways, is used for generating target angles for the robot joints. A generalized Matsuoka oscillator [13] is used for modeling the CPGs and the CPG network parameters are found by an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks

Auddy

Magg

Wermter

2019

2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion. However, most existing CPG-based approaches do not address the problem of high-level control explicitly. We propose a novel hierarchical control mechanism for bipedal locomotion where an optimized CPG network is used for joint control and a neural network acts as a high-level controller for modulating the CPG network. By separating motion generation from motion modulation, the high-level controller does not need to control individual joints directly but instead can develop to achieve a higher goal using a low-dimensional control signal. The feasibility of the hierarchical controller is demonstrated through simulation experiments using the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the controller's ability to function even without the availability of an exact robot model.

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“…In addition, a Genetic Algorithm was used to find optimal parameters for the CPG network in open-loop [25]. Likewise, Cristiano et al drove the NAO robot to walk on unknown sloped terrains by a robust CPG network based on Matsuoka's oscillators that directly control the angular position of the robot's joints [26].…”

Section:  Applications Of Cpg-based Control In Roboticsmentioning

confidence: 99%

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

2017

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Abstract:In this paper, we intend to improve the CPG network presented by Pinto et al. based on 4-cell model for bipedal locomotion systems. This model is composed of four coupled identical cells which internal dynamics of each one is described by the Morris-Lecar nonlinear differential equation and the couplings between the cells follow the synaptic type. We exploit an elitist non-dominated sorting genetic algorithm (NSGA II) to find the best set of coupling weights by which the phase differences become optimally close to the ones required for a primary bipedal gait. Thus, we achieve the rhythmic signals associated with four primary bipedal gaits of walk, run, two-legged jump, and two-legged hop. Also, we successfully obtain all secondary gaits corresponding to the bipedal locomotion identified by Pinto et al. from the 4-cell model, by symmetry breaking bifurcations of primary gaits. Particularly, we are able to produce the secondary gait called "hesitation walk" through transition from primary gaits of run and two-legged jump.

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Locomotion Control of a Biped Robot through a Feedback CPG Network

Cited by 7 publications

References 11 publications

Deterministic phase resetting with predefined response time for CPG networks based on Matsuoka’s oscillator

Deterministic phase resetting with predefined response time for CPG networks based on Matsuoka’s oscillator

Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks

Gait Generation and Transition for Bipedal Locomotion System Using Morris-Lecar Model of Central Pattern Generator

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