Energy Efficiency of a Quadruped Robot with Neuro-Inspired Control in Complex Environments

Arena, Paolo; Patané, Luca; Taffara, Salvatore

doi:10.3390/en14020433

Cited by 16 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both algorithms require offline training. Arena et al (2021) implemented an adaptive controller based on the FitzHugh-Nagumo neuron to reduce the energy consumption of a model of Mini MIT cheetah while the robot walked on uneven grounds. They found the optimal values of the parameters of the trajectory for five different values of slopes and employed a state machine to update the parameters of the trajectory with the optimal parameters of the closest slope.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting

et al. 2023

View full text Add to dashboard Cite

Animals adjust their leg stiffness and stride angle in response to changing ground conditions and gait parameters, resulting in improved stability and reduced energy consumption. This paper presents an online learning algorithm that attempts to mimic such animal behavior by maximizing energy efficiency on the fly or equivalently, minimizing the cost of transport of legged robots by adaptively changing the leg stiffness and stride angle while the robot is traversing on grounds with unknown characteristics. The algorithm employs an approximate stochastic gradient method to change the parameters in real-time, and has the following advantages: (1) the algorithm is computationally efficient and suitable for real-time operation; (2) it does not require training; (3) it is model-free, implying that precise modeling of the robot is not required for good performance; and (4) the algorithm is generally applicable and can be easily incorporated into a variety of legged robots with adaptable parameters and gaits beyond those implemented in this paper. Results of exhaustive performance assessment through numerical simulations and experiments on an under-actuated quadruped robot with compliant legs are included in the paper. The robot platform used a pneumatic piston in each leg as a variable, passive compliant element. Performance evaluation using simulations and experiments indicated that the algorithm was capable of converging to near-optimal values of the cost of transport for given operating conditions, terrain properties, and gait characteristics with no prior knowledge of the terrain and gait conditions. The simplicity of the algorithm and its demonstrably improved performance make the approach of this paper an excellent candidate for adaptively controlling tunable parameters of compliant, legged robots.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Both algorithms require offline training. Arena et al. (2021) implemented an adaptive controller based on the FitzHugh-Nagumo neuron to reduce the energy consumption of a model of Mini MIT cheetah while the robot walked on uneven grounds.…”

Section: Related Workmentioning

confidence: 99%

An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The quadruped robot will adopt different gait to adapt to the environment in different terrain conditions, and the motion speed, stability, and energy consumption of different gait are also different (Arena et al , 2021). Therefore, the fast and smooth gait transformation of quadruped robot is an important research problem.…”

Section: Central Pattern Generator Gait Generatormentioning

confidence: 99%

A CPG-based gait planning and motion performance analysis for quadruped robot

Wei

Liu

et al. 2022

View full text Add to dashboard Cite

Purpose To achieve stable gait planning and enhance the motion performance of quadruped robot, this paper aims to propose a motion control strategy based on central pattern generator (CPG) and back-propagation neural network (BPNN). Design/methodology/approach First, the Kuramoto phase oscillator is used to construct the CPG network model, and a piecewise continuous phase difference matrix is designed to optimize the duty cycle of walk gait, so as to realize the gait planning and smooth switching. Second, the mapper between CPG output and joint drive is established based on BP neural network, so that the quadruped robot based on CPG control has better foot trajectory to enhance the motion performance. Finally, to obtain better mapping effect, an evaluation function is resigned to evaluate the proximity between the actual foot trajectory and the ideal foot trajectory. Genetic algorithm and particle swarm optimization are used to optimize the initial weights and thresholds of BPNN to obtain more accurate foot trajectory. Findings The method provides a solution for the smooth gait switching and foot trajectory of the robot. The quintic polynomial trajectory is selected to testify the validity and practicability of the method through simulation and prototype experiment. Originality/value The paper solved the incorrect duty cycle under the walk gait of CPG network constructed by Kuramoto phase oscillator, and made the robot have a better foot trajectory by mapper to enhance its motion performance.

show abstract

“…In recent years, quadruped robot motion control in complex terrain has become a hot topic. [1][2][3] In the face of unstructured terrain, footed robots have stronger environmental adaptability than crawlers and wheeled robots. Additionally, quadruped robots are more stable than bipeds and simpler than hexapods.…”

Section: Introductionmentioning

confidence: 99%

Research on fusion control of sensor information and biological reflection based on CPG

Chen

Zhang

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To enhance the environmental adaptability of the quadruped robot and realize its stability in terrain with obstacles, this paper studies the fusion of sensor information feedback and bio-reflection control algorithm. First, a central pattern generator (CPG) that generates basal rhythmic movements is constructed. Secondly, a simplified foot end that can be used to identify the touchdown and obstacle information is designed. To enhance the stability of the quadruped robot during the flexor reflex process and avoid the problem of solidification in the reflex process, the foot touch signal was introduced into the flexor reflex algorithm, which enhanced the flexibility of the reflex process. To avoid secondary collisions with obstacles, the postswing reflection process is optimized, and active obstacle avoidance is realized after the obstacle is touched. Finally, the organic fusion of sensor information feedback, CPG control network, and bio-reflection control algorithm is realized. The simulation results show that the robot’s ability to overcome obstacles is improved, the number of reflections is less, and the reflection process is more stable.

show abstract

Energy Efficiency of a Quadruped Robot with Neuro-Inspired Control in Complex Environments

Cited by 16 publications

References 35 publications

An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting

An online learning algorithm for adapting leg stiffness and stride angle for efficient quadruped robot trotting

A CPG-based gait planning and motion performance analysis for quadruped robot

Research on fusion control of sensor information and biological reflection based on CPG

Contact Info

Product

Resources

About