Efficient hexapodal locomotion control based on flow-invariant subspaces

Arena, Eleonora; Arena, Paolo; Patané, Luca

doi:10.3182/20110828-6-it-1002.02533

Cited by 13 publications

(6 citation statements)

References 11 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: 87%

“…The overall network stability was theoretically proven exploiting tools from partial contraction theory on a network made of nonlinear oscillators with Laplacian couplings. 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: 87%

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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: 87%

Section: Motor Learning: Application To Climbingmentioning

confidence: 87%

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

Arena

Strauß

et al. 2017

Front. Neurorobot.

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“…In future studies, we would like to improve our robot and controller, for example, by incorporating a variable flexibility mechanism in the body axis to conduct more complex tasks. In addition, various turning strategies have been developed in quadruped and hexapod robots to modulate the leg movements for turning using bioinspired approaches based on central pattern generators, sensory systems [5], [22], [32], [44], [50], [57], and optimization techniques [14], [41], [56]. We would like to improve our instability-based strategy in the body-axis movement based on these strategies to enhance the turning maneuverability of multilegged robots in the future.…”

Section: Discussionmentioning

confidence: 99%

Advanced Turning Maneuver of a Many-Legged Robot Using Pitchfork Bifurcation

et al. 2022

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Legged robots have excellent terrestrial mobility for traversing diverse environments and, thus, have the potential to be deployed in a wide variety of scenarios. However, they are susceptible to falling and leg malfunction during locomotion. Although the use of a large number of legs can overcome these problems, it makes the body long and leads to many legs being constrained to contact with the ground to support the long body, which impedes maneuverability. To improve the locomotion maneuverability of such robots, this study focuses on dynamic instability, which induces rapid and large movement changes, and uses a 12-legged robot with a flexible body axis. Our previous work found that the straight walk of the robot becomes unstable through a Hopf bifurcation when the body-axis flexibility is changed, which induces body undulations. Furthermore, we developed a simple controller based on the Hopf bifurcation and showed that the instability facilitates the turning of the robot. In this study, we newly found that the straight walk becomes unstable through a pitchfork bifurcation when the body-axis flexibility is changed in a way different from that in our previous work. In addition, the pitchfork bifurcation induces a transition into a curved walk, whose curvature can be controlled by the body-axis flexibility. We developed a simple controller based on the pitchfork bifurcation characteristics and demonstrated that the robot can perform a turning maneuver superior to that with the previous controller. This study provides a novel design principle

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“…Otherwise, the redundant limbs of hexapod robots are helpful to deal with problems of limb damage. Numerous typical hexapod robots have been developed [5][6][7]. The research on these hexapod robots provides valuable experience for the following studies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot

et al. 2023

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To implement the various movement control of the hexapod robot, a motion controller based on the double-layered central pattern generator (CPG) is proposed in this paper. The novel CPG network is composed of a rhythm layer and a pattern layer. The CPG neurons are constructed based on Kuramoto nonlinear oscillator. The parameters including the frequency, coupling strength, and phase difference matrix of the CPG network for four typical gaits are planned. The mapping relationship between the signals of the CPG network and the joint trajectories of the hexapod robot is designed. The co-simulations and experiments have been conducted to verify the feasibility of the proposed CPG-based controller. The actual average velocities of the wave gait, the tetrapod gait, the tripod gait, and the self-turning gait are 10.8 mm/s, 25.5 mm/s, 37.8 mm/s and 26°/s, respectively. The results verify that the hexapod robot with the proposed double-layered CPG-based controller can perform stable and various movements.

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Efficient hexapodal locomotion control based on flow-invariant subspaces

Cited by 13 publications

References 11 publications

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

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

Advanced Turning Maneuver of a Many-Legged Robot Using Pitchfork Bifurcation

A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot

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