Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Model-Based Proprioceptive State Estimation for Spring-Mass Running
Date
2012-01-01
Author
Gur, Ozlem
Saranlı, Uluç
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
1
views
0
downloads
Autonomous applications of legged platforms will inevitably require accurate state estimation both for feedback control as well as mapping and planning. Even though kinematic models and low-bandwidth visual localization may be sufficient for fully-actuated, statically stable legged robots, they are in-adequate for dynamically dexterous, underactuated platforms where second order dynamics are dominant, noise levels are high and sensory limitations are more severe. In this paper, we introduce a model based state estimation method for dynamic running behaviors with a simple spring-mass runner. By using an approximate analytic solution to the dynamics of the model within an Extended Kalman filter framework, the estimation accuracy of our model remains accurate even at low sampling frequencies. We also propose two new event-based sensory modalities that further improve estimation performance in cases where even the internal kinematics of a robot cannot be fully observed, such as when flexible materials are used for limb designs. We present comparative simulation results to establish that our method outperforms traditional approaches which rely on constant acceleration motion models and that it eliminates the need for an extensive and unrealistic sensor suite.
Subject Keywords
Locomotion
,
Localization
,
Robot
,
Attitude estimation
URI
https://hdl.handle.net/11511/53509
Journal
ROBOTICS: SCIENCE AND SYSTEMS VII
Collections
Department of Computer Engineering, Article