Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators

Wang, Gao; Wang, Zhuo; Huang, Bo; Gan, Yong; Min, Feiyan

doi:10.1109/lra.2023.3258697

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…In EKF, two models are required from the robots, specifically the state space model and the observation model. In this study, the robot moves on a flat plane, and the state of the robot is defined by matrix (10). Since the robot moves omnidirectionally, the control signal 𝑢 is defined as the vector of position and orientation velocities, as depicted in Figure 8.…”

Section: System Designmentioning

confidence: 99%

See 1 more Smart Citation

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

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Section: System Designmentioning

confidence: 99%

“…The use of modeling in the EKF process includes kinematic and dynamic models [9], [10]. Kinematic models represent movement without considering the causes of that movement, while dynamic models take into account the forces acting on the system.…”

Section: Introductionmentioning

confidence: 99%

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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show abstract

“…The static threshold method generally sets a fixed threshold, and its range is larger than the statistical peak value of previous experimental values. For external torque acquisition, besides the direct estimation method (Suita et al, 1995) and the estimation method based on harmonic reducer deformation (Wang et al, 2023), numerous observer-based estimation methods have been proposed (De Luca et al, 2006;Haddadin, 2013;De Luca and Mattone, 2005;Duan et al, 2023;Ren et al, 2018). However, collision detection based on these external torque estimation methods needs to be more sensitive.…”

Section: Introductionmentioning

confidence: 99%

“…For external torque acquisition, besides the direct estimation method (Suita et al. , 1995) and the estimation method based on harmonic reducer deformation (Wang et al. , 2023), numerous observer-based estimation methods have been proposed (De Luca et al.…”

Section: Introductionmentioning

confidence: 99%

Real-time collision detection based on external torque mutation suppression and time series analysis

Zhang,

Luo,

Liu

et al. 2024

Self Cite

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Purpose As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection. Design/methodology/approach The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques. Findings Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy. Originality/value Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

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“…To ensure the stability and control accuracy of AUV underwater motion, a variety of control algorithms are applied to the AUV system. For example, PID control and its improved algorithm [4], fuzzy control method [5], neural network method [6], linear quadratic regulator (LQR)-based control method [7], sliding mode control method [8], adaptive control method [9,10], robust control method [11], etc. The PID-improved method has good robustness and transient response, but it cannot deal with the disturbance of the external environment quickly and effectively [4].…”

Section: Introductionmentioning

confidence: 99%

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Yuan,

She,

Zhang

et al. 2023

JMSE

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This study focuses on addressing the the coupling problem of the vertical and horizontal plane of an autonomous underwater vehicle (AUV) with an X-rudder. To guarantee the steering performance of the AUV, a depth and course control algorithm based on an L1 adaptive control algorithm (L1AC) is proposed. Firstly, the linear quadratic regulator (LQR) is designed on the basis of an AUV vertical and horizontal linear model, and the system has optimal control quality for both of the nominal vertical and horizontal models. Secondly, the nonlinear state predictor, disturbance estimator, and adaptive controller are designed separately, which is used for the compensation of the uncertainty and disturbance of the AUV dynamic. Finally, the rudder angle allocation algorithm is designed to realize the rudder angle command of the cross rudder to the X-rudder. The simulation and experiment results show that the adaptive control algorithm proposed in this paper can effectively control AUV to complete high-performance course and depth control. Based on the experiments, the prospects and shortcomings of the L1AC, LQR and MRAC algorithm in AUV control are compared and analyzed.

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Active Compliance Control Based on EKF Torque Fusion for Robot Manipulators

Cited by 7 publications

References 21 publications

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Real-time collision detection based on external torque mutation suppression and time series analysis

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

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