Performance analysis and terrain classification for a legged robot over rough terrain

2012 ◽  
Author(s):  
Fernando L. Garcia Bermudez ◽  
Ryan C. Julian ◽  
Duncan W. Haldane ◽  
Pieter Abbeel ◽  
Ronald S. Fearing
Keyword(s):  
Performance Analysis ◽  
Rough Terrain ◽  
Legged Robot ◽  
Terrain Classification

scholarly journals Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

2021 ◽  
Vol 45 (6) ◽  
pp. 843-857
Author(s):  
Russell Buchanan ◽  
Jakub Bednarek ◽  
Marco Camurri ◽  
Michał R. Nowicki ◽  
Krzysztof Walas ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Estimation Error ◽  
Extreme Environments ◽  
Legged Robot ◽  
Terrain Classification ◽  
Localization Algorithm ◽  
Geometric Information ◽  
Contact Points ◽  
Monte Carlo Localization ◽  
Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

Passive Gripping Foot for a Legged Robot to Move Over Rough Terrain

2021 ◽  
pp. 203-212
Author(s):  
Sho Hakamada ◽  
Sadayoshi Mikami
Keyword(s):  
Rough Terrain ◽  
Legged Robot

scholarly journals SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

2019 ◽  
Vol 9 (9) ◽  
pp. 1779 ◽  
Author(s):  
Yaguang Zhu ◽  
Chaoyu Jia ◽  
Chao Ma ◽  
Qiong Liu
Keyword(s):  
Legged Robot ◽  
Terrain Classification ◽  
Walking Robot ◽  
Adaptive Locomotion ◽  
Speeded Up Robust Features ◽  
Pixel Image ◽  
Series Of Experiments ◽  
Multilegged Walking Robot ◽  
Local Image

In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.

Enhancing Legged Robot Navigation of Rough Terrain via Tail Tapping

2021 ◽  
pp. 213-225
Author(s):  
Daniel Soto ◽  
Kelimar Diaz ◽  
Daniel I. Goldman
Keyword(s):  
Robot Navigation ◽  
Rough Terrain ◽  
Legged Robot

scholarly journals Search-based planning for a legged robot over rough terrain

2009 ◽  
Author(s):  
P. Vernaza ◽  
M. Likhachev ◽  
S. Bhattacharya ◽  
S. Chitta ◽  
A. Kushleyev ◽  
...  
Keyword(s):  
Rough Terrain ◽  
Legged Robot

Analysis of the Energy Loss on Quadruped Robot Having a Flexible Trunk Joint

2017 ◽  
Vol 29 (3) ◽  
pp. 536-545
Author(s):  
Masahiro Ikeda ◽  
◽  
Ikuo Mizuuchi
Keyword(s):  
Energy Loss ◽  
Energy Flow ◽  
Control Method ◽  
Quadruped Robot ◽  
Rough Terrain ◽  
Legged Robot ◽  
Walking Robot ◽  
Passive Joint ◽  
Quadruped Robots ◽  
The Relationship

[abstFig src='/00290003/09.jpg' width='300' text='Energy flow in legged robot' ] As a method of robot movement, legs have the advantage of traversability on rough terrain. However, the motion of a legged robot is accompanied by energy loss. The main causes for this loss could be negative work and contact between the legs and ground. On the other hand, animals with legs are considered to reduce energy loss by using the elasticity of their body. In this study, we analyze the influence of walking, using an elastic passive joint mounted on the trunk of a quadruped robot, on the energy loss. Additionally, we study the energy flow between legs and elastic components. In this study, we clarify a control method for quadruped robots in order to reduce the energy loss of walking. The results of simulating a quadruped walking robot, which has passive joints with elastic components on the trunk, are analyzed and the relationship between each kind of energy loss and the trunk joint’s elasticity is clarified.

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