Context Identification for Efficient Multiple-Model State Estimation of Systems With Cyclical Intermittent Dynamics

Skaff, Sarjoun; Choset, Howie; Tesch, Matthew

doi:10.1109/tro.2010.2073011

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…In contrast to previous studies like [35,36], this study is focus in the alternating tripod gait and not in the analysis with dynamics gaits like jogging.…”

Section: Discussionmentioning

confidence: 99%

“…This is a largely due to the difficulty of obtaining a robust method to analyze and compute the pose and orientation of the robot. Some developments were carried out to try to estimate the displacement of the robot with proprioceptive sensors [34][35][36] or with the use of external sensors [37][38][39][40][41][42]. However, none of the previous works have made a sensor fusion with an odometry algorithm to obtain a better and more robust pose estimation.…”

Section: Introductionmentioning

confidence: 99%

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A Sensor Fusion Method for Pose Estimation of C-Legged Robots

León

Cebolla

Barrientos

2020

Sensors

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In this work the authors present a novel algorithm for estimating the odometry of “C” legged robots with compliant legs and an analysis to estimate the pose of the robot. Robots with “C” legs are an alternative to wheeled and tracked robots for overcoming obstacles that can be found in different scenarios like stairs, debris, etc. Therefore, this kind of robot has become very popular for its locomotion capabilities, but at this point these robots do not have developed algorithms to implement autonomous navigation. With that objective in mind, the authors present a novel algorithm using the encoders of the legs to improve the estimation of the robot localization together with other sensors. Odometry is necessary for using some algorithms like the Extended Kalman Filter, which is used for some autonomous navigation algorithms. Due to the flexible properties of the “C” legs and the localization of the rotational axis, obtaining the displacement at every step is not as trivial as in a wheeled robot; to solve those complexities, the algorithm presented in this work makes a linear approximation of the leg compressed instead of calculating in each iteration the mechanics of the leg using finite element analysis, so the calculus level is reduced. Furthermore, the algorithm was tested in simulations and with a real robot. The results obtained in the tests are promising and together with the algorithm and fusion sensor can be used to endow the robots with autonomous navigation.

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“…In contrast to previous studies like [35,36], this study is focus in the alternating tripod gait and not in the analysis with dynamics gaits like jogging.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Sensor Fusion Method for Pose Estimation of C-Legged Robots

León

Cebolla

Barrientos

2020

Sensors

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“…Research on state estimation for legged robots started less than 15 years ago. [6][7] [16] developed the earliest legged robot state estimation algorithms on RHex [15]. However, these works were highly constrained by RHex's mechanical design, and they could not be generalized to other robots or more difficult terrains.…”

Section: Related Workmentioning

confidence: 99%

State Estimation for Legged Robots Using Contact-Centric Leg Odometry

Yang,

Kumar,

et al. 2019

Preprint

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Our goal is to send legged robots into challenging, unstructured terrains that wheeled systems cannot traverse. Moreover, precise estimation of the robot's position and orientation in rough terrain is especially difficult. To address this problem, we introduce a new state estimation algorithm which we term Contact-Centric Leg Odometry (COCLO). This new estimator uses a Square Root Unscented Kalman Filter (SR-UKF) to fuse multiple proprioceptive sensors available on a legged robot. In contrast to IMU-centric filtering approaches, COCLO formulates prediction and measurement models according to the contact status of legs. Additionally, COCLO has an indirect measurement model using joint velocities to estimate the robot body velocity. In rough terrain, when IMUs suffer from large amounts of noise, COCLO's contactcentric approach outperforms previous IMU-centric methods. To demonstrate improved state estimation accuracy, we compare COCLO with Visual Inertial Navigation System (VINS), a state-of-the-art visual inertial odometry [13], in three different environments: flat ground, ramps, and stairs. COCLO achieves better estimation precision than VINS in all three environments and is robust to unstable motion. Finally, we also show that COCLO and a modified VINS can work in tandem to improve each other's performance.

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“…Even in their more relaxed topological representations [16], such methods are committed to repeated measurements as a necessary means of discovery, even when used on legged platforms [11]. However, the dynamics of locomotion inherent to dexterous machines such as the legged robot used in this work complicate considerably the task of accurately estimating state or building a world model [17,18]. Here, contrarily, given the very much more narrow requirements of the task at hand, we are able to presume a priori knowledge of a "perfect" model (Section II-B).…”

Section: B Contributionsmentioning

confidence: 99%

Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator. Abstract -This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator.

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Context Identification for Efficient Multiple-Model State Estimation of Systems With Cyclical Intermittent Dynamics

Cited by 7 publications

References 19 publications

A Sensor Fusion Method for Pose Estimation of C-Legged Robots

A Sensor Fusion Method for Pose Estimation of C-Legged Robots

State Estimation for Legged Robots Using Contact-Centric Leg Odometry

Autonomous legged hill and stairwell ascent

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