CPG-based locomotion generation in a Drosophila inspired legged robot

Arena, Eleonora; Arena, Paolo; Patané, Luca

doi:10.1109/biorob.2012.6290809

Cited by 18 publications

(12 citation statements)

References 9 publications

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“…As demonstrated in previous studies, the network for gait control has a diffusive, undirected tree-graph configuration, which guarantees asymptotic phase stability independently of any imposed locomotion pattern (Arena et al, 2011; Arena E. et al, 2012). …”

Section: Motor Learning: Application To Climbingmentioning

confidence: 85%

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Arena

Strauß

et al. 2017

Front. Neurorobot.

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In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.

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Section: Motor Learning: Application To Climbingmentioning

confidence: 85%

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Arena

Strauß

et al. 2017

Front. Neurorobot.

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“…6 that refers to the middle leg controller. Without discussing in details the structure (see [25] for a complete description), the CPG generates coordinated signals for the legs guiding the phase locking within the different gaits. These waveforms are adjusted to the kinematics of each leg through a set of parameters and used to control the position of coxa, femur and tibia joints of the robot.…”

Section: Modeling Motor Learningmentioning

confidence: 99%

A computational model for motor learning in insects

Arena

Caccamo

Patané

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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The aim of this paper is to propose a computational model, inspired by Drosophila melanogaster, able to handle problems related to motor learning. The role of the Mushroom Bodies and the Central Complex in solving this problem is analyzed and plausible biologically inspired models are proposed. The designed computational models have been evaluated in simulation using a dynamic structure inspired by the fruit fly. The obtained results open the way to new neurobiological experiments focused to better understand the underlined mechanisms involved, to verify the feasibility of the hypotheses formulated and the significance of the obtained results.

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“…We can apply the same strategy to the tail taking into consideration the desired robot speed and the relations between the oscillator frequency and the tail wave length. The stability of locomotion gaits can be treated using the Contraction Theory, introduced in [53], and later on extended to Partial Contraction [54] that we already successfully applied in a hexapod robot whose CPG was developed using the same basic oscillators [28,55]. This efficiently handles the problem with exponential convergence to behaviors and to specific relations among the state variables of the control system.…”

Section: Locomotion Control Systemmentioning

confidence: 99%

“…For the gait generation, we adopted a connection scheme based on diffusive coupling to obtain stable phase relations among the connected cells. The control network represents a central pattern generator (CPG) able to guarantee the coordination among the different actuators present in the robot obtaining stable locomotion patterns [27,28].…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Robotic Solutions for Landslide Monitoring

Patané

2019

Energies

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Bio-inspired solutions are often taken into account to solve problems that nature took millions of years to deal with. In the field of robotics, when we need to design systems able to perform in unstructured environments, bio-inspiration can be a useful instrument both for mechanical design and for the control architecture. In the proposed work the problem of landslide monitoring is addressed proposing a bio-inspired robotic structure developed to deploy a series of smart sensors on target locations with the aim of creating a sensor network capable of acquiring information on the status of the area of interest. The acquired data can be used both to create models and to generate alert signals when a landslide event is identified in the early stage. The design process of the robotic system, including dynamic simulations and robot experiments, will be presented here.

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CPG-based locomotion generation in a Drosophila inspired legged robot

Cited by 18 publications

References 9 publications

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

A computational model for motor learning in insects

Bio-Inspired Robotic Solutions for Landslide Monitoring

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