A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors

Cantelli, Luciano; Muscato, Giovanni; Nunnari, Marco; Spina, Davide

doi:10.1109/tmech.2014.2385940

Cited by 32 publications

(16 citation statements)

References 16 publications

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“…Generally, the drift error can be corrected by a magnetometer; however, it cannot be used for robotic applications because of the magnetic distortions caused by electrical actuators and the indoor environment, such as steel window frames or handrails on stairs. Therefore, in future studies, a more elaborate filter, such as the ZUPT algorithm [49] and the model-based extended Kalman filter [35,50], or an additional sensor should be considered to predict the gyroscope bias and correct drift errors. Finally, the exact position of the contact point between the exoskeleton and ground should be calculated.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, they have a relatively lower resolution than encoders; thus, measuring a joint angle using an AHRS or IMU is considered inappropriate in robotics. Nevertheless, several studies [34,35] estimated the joint angle of a manipulator with AHRSs and IMUs to utilize their advantages of contactless sensing and simple installation. Additionally, as an AHRS returns the attitude as an Euler angle consisting of three independent variables (roll, pitch, and yaw), it can estimate up to three joint angles without installing encoders on all the joint axes to be measured [36,37].…”

Section: Methodsmentioning

confidence: 99%

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Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure

Shim

Han

Choi

et al. 2019

Sensors

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While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure

Shim

Han

Choi

et al. 2019

Sensors

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“…An up to 78% improvement in pose accuracy of the robot manipulator was reported. Moreover, a joint-angle estimation method using low-cost inertial sensors was presented by Cantelli et al [32]. Specifically, three cascaded EKFs have been used to estimate the joint angles by the fusion of the outputs of tri-axial gyroscopes and accelerometers.…”

Section: Localisation Mapping and Tracking In Hrcmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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“…In particular, the estimation of link states is important because most industrial manipulators are not usually equipped with sensors. To this end, research has been conducted [ 4 , 9 , 10 ]. However, it is still difficult to obtain link information because the motor position is only measurable and the manipulators demonstrate flexibility between the motor and link.…”

Section: Introductionmentioning

confidence: 99%

Position and Velocity Estimation for Two-Inertia System with Nonlinear Stiffness Based on Acceleration Sensor

Nam

Lee

Kuc

et al. 2015

Sensors

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In this paper, we consider the state estimation problem for flexible joint manipulators that involve nonlinear characteristics in their stiffness. The two key ideas of our design are that (a) an accelerometer is used in order that the estimation error dynamics do not depend on nonlinearities at the link part of the manipulators and (b) the model of the nonlinear stiffness is indeed a Lipschitz function. Based on the measured acceleration, we propose a nonlinear observer under the Lipschitz condition of the nonlinear stiffness. In addition, in order to effectively compensate for the estimation error, the gain of the proposed observer is chosen from the ARE (algebraic Riccati equations) which depend on the Lipschitz constant. Comparative experimental results verify the effectiveness of the proposed method.

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A Joint-Angle Estimation Method for Industrial Manipulators Using Inertial Sensors

Cited by 32 publications

References 16 publications

Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure

Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure

Symbiotic human-robot collaborative assembly

Position and Velocity Estimation for Two-Inertia System with Nonlinear Stiffness Based on Acceleration Sensor

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