CPG control model of snake-like robot parameters of optimization based on GA

Wen, Pei; Xu, Linsen; Feng, Bin; Zhong, Wang

doi:10.1109/robio.2015.7419057

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…GAPSO is used in this paper. In this algorithm, GA is introduced into particle swarm optimization [32,33] and then the crossover and mutation steps in the genetic algorithm are used to obtain a better next-generation population after the PSO algorithm finds the optimal individual and population. The GAPSO algorithm not only retains the original location transfer of the PSO algorithm but also integrates the powerful global search ability of the genetic algorithm, making the optimization process more efficient.…”

Section: Optimization Study On the Similarity Of Forward Swimming Pos...mentioning

confidence: 99%

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

Cao

et al. 2022

JMSE

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The current development of manta ray robots is usually based on functional bionics, and there is a lack of bionic research to enhance the similarity of motion posture. To better exploit the characteristics of bionic, a similarity evaluation rule is constructed herein by a Dynamic Time Warping (DTW) algorithm to guide the optimization of the control parameters of a manta ray robot. The Central Pattern Generator (CPG) network with time and space asymmetry oscillation characteristics is improved to generate coordinated motion control signals for the robot. To optimize similarity, the CPG network is optimized with the genetic algorithm and particle swarm optimization (GAPSO) to solve the problems of multiple parameters, high non-linearity, and uncertain parameter coupling in the CPG network. The experimental results indicate that the similarity between the forward motion pose of the optimized manta ray robot and the manta ray is improved to 88.53%.

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Section: Optimization Study On the Similarity Of Forward Swimming Pos...mentioning

confidence: 99%

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

Cao

et al. 2022

JMSE

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“…Previous studies have demonstrated the usefulness of biomimetic movement templates such as movement primitives and CPGs in robots [27]- [34]. The Hodgkin-Huxley model of action potential generation in single neurons [35], [36] has been used for developing locomotion strategies in snake-like [37], [38] and quadruped robots [39]. The Matsuoka model [40] has been used to produce robot locomotion [17], [41].…”

Section: Introductionmentioning

confidence: 99%

DeepCPG Policies for Robot Locomotion

Deshpande

Hurd

Minai

et al. 2023

IEEE Trans. Cogn. Dev. Syst.

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Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take this inspiration for developing walking behaviors in multi-legged robots. We present novel DeepCPG policies that embed CPGs as a layer in a larger neural network and facilitate end-to-end learning of locomotion behaviors in deep reinforcement learning (DRL) setup. We demonstrate the effectiveness of this approach on physics engine-based insectoid robots. We show that, compared to traditional approaches, DeepCPG policies allow sample-efficient end-to-end learning of effective locomotion strategies even in the case of high-dimensional sensor spaces (vision). We scale the DeepCPG policies using a modular robot configuration and multiagent DRL. Our results suggest that gradual complexification with embedded priors of these policies in a modular fashion could achieve non-trivial sensor and motor integration on a robot platform. These results also indicate the efficacy of bootstrapping more complex intelligent systems from simpler ones based on biological principles. Finally, we present the experimental results for a proof-of-concept insectoid robot system for which DeepCPG learned policies initially using the simulation engine and these were afterwards transferred to real-world robots without any additional fine-tuning.

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“…CPG is a neural network that exists in the spinal cords of living forms which is utilized as an activation signal for muscle contractions, i.e. mechanical joints in the robot [24].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Snake-like Robot Locomotion Using GA: Serpenoid Design

Hůlka¹,

Matoušek²,

Dobrovský³

et al. 2020

mendel

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This work investigates the locomotion efficiency of snake-like robots through evolutionary optimization using the simulation framework PhysX (NVIDIA). The Genetic Algorithm (GA) is used to find the optimal forward head serpentine gait parameters, and the snake speed is taken into consideration in the optimization. A fitness function covering robot speed is based on a complex physics simulation in PhysX. A general serpenoid form is applied to each joint. Optimal gait parameters are calculated for a virtual model in a simulation environment. The fitness function evaluation uses the Simulation In the Loop (SIL) technique, where the virtual model is an approximation of a real snake-like robot. Experiments were performed using an 8-link snake robot with a given mass and a different body friction. The aim of the optimization was speed and length of the trace.

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CPG control model of snake-like robot parameters of optimization based on GA

Cited by 4 publications

References 7 publications

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

Similarity Evaluation Rule and Motion Posture Optimization for a Manta Ray Robot

DeepCPG Policies for Robot Locomotion

Optimization of Snake-like Robot Locomotion Using GA: Serpenoid Design

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