Online optimal gait generation for bipedal walking robots using legendre pseudospectral optimization

2016 ◽  
Author(s):  
Ayonga Hereid ◽  
Shishir Kolathaya ◽  
Aaron D. Ames
Keyword(s):  
Bipedal Walking ◽  
Walking Robots ◽  
Gait Generation ◽  
Optimal Gait

Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks

2019 ◽  
pp. 16-39 ◽  
Author(s):  
Sergei Savin ◽  
Aleksei Ivakhnenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Bipedal Walking ◽  
Training Dataset ◽  
Walking Robots ◽  
Foot Placement ◽  
Gait Generation ◽  
Free Region

In this chapter, the problem of finding a suitable foothold for a bipedal walking robot is studied. There are a number of gait generation algorithms that rely on having a set of obstacle-free regions where the robot can step to and there are a number of algorithms for generating these regions. This study breaches the gap between these algorithms, providing a way to quickly check if a given obstacle free region is accessible for foot placement. The proposed approach is based on the use of a classifier, constructed as a convolutional neural network. The study discusses the training dataset generation, including datasets with uncertainty related to the shapes of the obstacle-free regions. Training results for a number of different datasets and different hyperparameter choices are presented and showed robustness of the proposed network design both to different hyperparameter choices as well as to the changes in the training dataset.

scholarly journals Preference-Based Learning for User-Guided HZD Gait Generation on Bipedal Walking Robots

2021 ◽  
Author(s):  
Maegan Tucker ◽  
Noel Csomay-Shanklin ◽  
Wen-Loong Ma ◽  
Aaron D. Ames
Keyword(s):  
Bipedal Walking ◽  
Walking Robots ◽  
Gait Generation

scholarly journals Optimal Gait Generation in Biped Locomotion of Humanoid Robot to Improve Walking Speed

Biped Robots ◽  
10.5772/13871 ◽  
2011 ◽  
Author(s):  
Hanafiah Yussof ◽  
Mitsuhiro Yamano ◽  
Yasuo Nasu ◽  
Masahiro Ohk
Keyword(s):  
Humanoid Robot ◽  
Walking Speed ◽  
Biped Locomotion ◽  
Gait Generation ◽  
Optimal Gait

ZMP-Based Trajectory Generation for Bipedal Robots Using Quadratic Programming

2020 ◽  
pp. 266-285
Author(s):  
Sergei Savin
Keyword(s):  
Numerical Simulation ◽  
Quadratic Programming ◽  
Center Of Mass ◽  
Trajectory Generation ◽  
Bipedal Walking ◽  
Walking Robots ◽  
Bipedal Robots ◽  
Change Of Variables ◽  
Viable Solution ◽  
Modern Optimization

In this chapter, the problem of trajectory generation for bipedal walking robots is considered. A number of modern techniques are discussed, and their limitations are shown. The chapter focuses on zero-moment point methods for trajectory generation, where the desired trajectory of that point can be used to allow the robot to keep vertical stability if followed, and presents an instrument to calculate the desired trajectory for the center of mass for the robot. The chapter presents an algorithm based on quadratic programming, with an introduction of a slack variable to make the problem feasible and a change of variables to improve the numeric properties of the resulting optimization problem. Modern optimization tools allow one to solve such problems in real time, making it a viable solution for trajectory planning for the walking robots. The chapter shows a few results from the numerical simulation made for the algorithm, demonstrating its properties.

Optimal Gait Design for Systems With Drift on SO(3)

2013 ◽  
Author(s):  
Matthew Travers ◽  
Howie Choset
Keyword(s):  
Angular Momentum ◽  
Control Method ◽  
Three Dimensional ◽  
Free Flight ◽  
Gait Generation ◽  
Geometric Methods ◽  
Wide Range ◽  
Optimal Gait ◽  
Computational Reduction ◽  
Inertial Systems

Geckos that jump, cats that fall, and satellites that are inertially controlled fundamentally locomote in the same way. These systems are bodies in free flight that actively reorientate under the influence of conservation of angular momentum. We refer to such bodies as inertial systems. This work presents a novel control method for inertial systems with drift that combines geometric methods and computational control. In previous work, which focused on inertial systems starting from rest, a set of visual tools was developed that readily allowed on to design gaits. A key insight of this work was deriving coordinates, called minimum perturbation coordinates, which allowed the visual tools to be applied to the design of a wide range of motions. This paper draws upon the same insight to show that it is possible to approximately analyze the kinematic and dynamic contributions to net motion independently. This approach is novel because it uses geometric tools to support computational reduction in automatic gait generation on three-dimensional spaces.

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