A Context-Based State Estimation Technique for Hybrid Systems

Skaff, Sarjoun; Choset, Howie; Lin, Pei-Chun

doi:10.1109/robot.2005.1570720

Cited by 11 publications

(12 citation statements)

References 14 publications

(10 reference statements)

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“…This is similar to the Interacting Multiple Model approach of Ref. [10] that switches estimation models between flight and stance.…”

Section: B Estimation Techniquesmentioning

confidence: 86%

“…This is often addressed through the use of an integrative approach that estimates the values by combining information from secondary modalities. Such sources vary the gamut and include GPS (Global Positioning System), line-vision [7], stereo vision [6], SLAM (Simultaneous Localization and Mapping) [9], leg kinematics [10], and range measurements to a ground surface [11].…”

Section: Introductionmentioning

confidence: 99%

“…This leads to incomplete measurements and a saturation of the sensing apparatus. Integrative approaches using ground range measurements [11] and leg kinematics [10] have been introduced in this domain; however, such approaches are not ideal as they require a ground plane assumption.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optical Flow Aided Motion Estimation for Legged Locomotion

Singh

Csonka

Waldron

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Dynamic legged locomotion entails navigating terrain at high speed. The impact shocks from rapid footfalls, pivotal for such mobility, introduce large impulses that saturate motion measurement. A biomimetic approach is presented in which visual information, in the form of optical flow, complements information from inertial sensors. The motion is then determined using a two-phase Hybrid Extended Kalman Filter. Experimentation in determining attitudes on a robotic leg platform shows a reduction in drift over inertial approaches and in delay over visual approaches. In tests with 6g impulses, pose was recovered within 5 deg rms with angular rate errors limited to 10 deg/sec at frequencies up to 250 Hz.

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“…This is similar to the Interacting Multiple Model approach of Ref. [10] that switches estimation models between flight and stance.…”

Section: B Estimation Techniquesmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optical Flow Aided Motion Estimation for Legged Locomotion

Singh

Csonka

Waldron

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Consequently, a number of hybrid estimation methods have been proposed for systems with multiple different phases with distinct dynamics [20] such as different leg contact configurations [31,28] or surface conditions [30]. In any case, the consideration of structurally different hybrid states of the system substantially improves performance [15,16].…”

Section: Related Workmentioning

confidence: 99%

Model-Based Proprioceptive State Estimation for Spring-Mass Running

Gur¹,

Saranlı²

2011

Robotics: Science and Systems VII

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Abstract-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 inadequate 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 1eliminates the need for an extensive and unrealistic sensor suite.

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“…the LPS's cues. We believe that the interacting multiple model approach [28] holds significant promise in this context.…”

Section: Discussionmentioning

confidence: 99%

Sensor Data Fusion for Body State Estimation in a Hexapod Robot with Dynamical Gaits

Lin

Komsuoğlu²,

Koditschek

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot's body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-We report on a hybrid 12-dimensional full body state estimator for a hexapod robot executing a jogging gait in steady state on level terrain with regularly alternating ground contact and aerial phases of motion. We use a repeating sequence of continuous time dynamical models that are switched in and out of an extended Kalman filter to fuse measurements from a novel leg pose sensor and inertial sensors. Our inertial measurement unit supplements the traditionally paired three-axis rate gyro and three-axis accelerometer with a set of three additional three-axis accelerometer suites, thereby providing additional angular acceleration measurement, avoiding the need for localization of the accelerometer at the center of mass on the robot's body, and simplifying installation and calibration. We implement this estimation procedure offline, using data extracted from numerous repeated runs of the hexapod robot RHex (bearing the appropriate sensor suite) and evaluate its performance with reference to a visual ground-truth measurement system, comparing as well the relative performance of different fusion approaches implemented via different model sequences. Index Terms-Extended

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A Context-Based State Estimation Technique for Hybrid Systems

Cited by 11 publications

References 14 publications

Optical Flow Aided Motion Estimation for Legged Locomotion

Optical Flow Aided Motion Estimation for Legged Locomotion

Model-Based Proprioceptive State Estimation for Spring-Mass Running

Sensor Data Fusion for Body State Estimation in a Hexapod Robot with Dynamical Gaits

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