New automated learning CPG for rhythmic patterns

Farzaneh, Yadollah; Akbarzadeh, Alireza; Akbari, Ali Akbar

doi:10.1007/s11370-012-0111-5

Cited by 8 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying some algebraic manipulations we have: C (q,q) = H (θ)θ 2 (20) where h i (θ) (i.e., the ith element of H(θ)) could be obtained from the following equation.…”

Section: Discussionmentioning

confidence: 99%

“…A sinusoidal joint trajectory is the simplest form that meets these properties and is the core of Phase Oscillators. Due to their simplicity, Phase Oscillators are extensively used in robotic applications [17][18][19][20]. Moreover, implementing Phase Oscillators using dynamical systems, such as Hopf and Van Der Pol oscillators [21], offers us stable limit cycles with advantageous properties such as smooth convergence behavior; see [22] for application in swimming robots.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Natural Oscillator to exploit natural dynamics for energy efficiency

Khoramshahi

Nasiri

Shushtari

et al. 2017

Robotics and Autonomous Systems

View full text Add to dashboard Cite

We present a novel adaptive oscillator, called Adaptive Natural Oscillator (ANO), to exploit the natural dynamics of a given robotic system. This tool is built upon the Adaptive Frequency Oscillator (AFO), and it can be used as a pattern generator in robotic applications such as locomotion systems. In contrast to AFO, that adapts to the frequency of an external signal, ANO adapts the frequency of reference trajectory to the natural dynamics of the given system. In this work, we prove that, in linear systems, ANO converges to the system's natural frequency. Furthermore, we show that this tool exploits the natural dynamics for energy efficiency through minimization of actuator effort. This property makes ANO an appealing tool for energy consumption reduction in cyclic tasks; especially in legged systems. We also extend the proposed adaptation mechanism to high dimensional and general cases; such as n-DOF manipulators. In addition, by investigating a hopper leg in simulation, we show the efficacy of ANO in face of dynamical discontinuities; such as those inherent in legged locomotion. Furthermore, we apply ANO to a simulated compliant robotic manipulator performing a periodic task where the energy consumption is drastically reduced. Finally, the experimental results on a 1-DOF compliant joint show that our adaptive oscillator, despite all practical uncertainties and deviations from theoretical models, exploits the natural dynamics and reduces the energy consumption.

show abstract

“…Applying some algebraic manipulations we have: C (q,q) = H (θ)θ 2 (20) where h i (θ) (i.e., the ith element of H(θ)) could be obtained from the following equation.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Adaptive Natural Oscillator to exploit natural dynamics for energy efficiency

Khoramshahi

Nasiri

Shushtari

et al. 2017

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…Many early methods relied heavily on the evolutionary computation method (e.g., genetic algorithm, genetic programming, and particle swarm optimization) [121]- [123]. In recent years, supervised and unsupervised learning techniques increasingly played a large role in CPG parameter search [124], [125]. For example, with the help of statistical learning techniques, oscillator and attractor models have been designed exactly with the goal to facilitate the process of tuning multiple movement primitives [26].…”

Section: B Parameter Tuningmentioning

confidence: 99%

A Survey on CPG-Inspired Control Models and System Implementation

Tan

Chen

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

245

114

View full text Add to dashboard Cite

This paper surveys the developments of the last 20 years in the field of central pattern generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging robotics-related applications. Functioning as a biological neural network, CPGs can be considered as a group of coupled neurons that generate rhythmic signals without sensory feedback; however, sensory feedback is needed to shape the CPG signals. The basic idea in engineering endeavors is to replicate this intrinsic, computationally efficient, distributed control mechanism for multiple articulated joints, or multi-DOF control cases. In terms of various abstraction levels, existing CPG control models and their extensions are reviewed with a focus on the relative advantages and disadvantages of the models, including ease of design and implementation. The main issues arising from design, optimization, and implementation of the CPG-based control as well as possible alternatives are further discussed, with an attempt to shed more light on locomotion control-oriented theories and applications. The design challenges and trends associated with the further advancement of this area are also summarized.

show abstract