Iterative learning control for a distributed cloud robot with payload delivery

Li, Jiehao; Wang, Shoukun; Wang, Junzheng; Li, Jing; Zhao, Jing; Ma, Liling

doi:10.1108/aa-11-2020-0179

Cited by 34 publications

(7 citation statements)

References 46 publications

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“…According to Gautier and Khalil [31] and Khalil and Bennis [32], some inertial parameters in ϕ are completely unidentifiable and some others can be only identified in linear combinations. Therefore, (3) can be further simplified as follows: τ = Y(q, q, q, α)π, (4) where π ∈ p×1 is the base parameter set. p is the element number in π. Y(q, q, q, α) ∈ n×p is the regressor matrix with full rank after removing the linearly correlated columns in W(q, q, q, α) ∈ n×14n .…”

Section: Preliminariesmentioning

confidence: 99%

“…With the improvement of the ratio from the payload mass to the manipulator mass, the robot payload brings about a much larger contribution to the actuator torques and has an increasing importance in robotics [1]. For example, the inertial parameters of the payload can provide a strong priori for planning and controlling of robots [2][3][4]. In addition, inaccurate payload inertial parameters can lead to approximately 50% of robot overloading in path planning [5].…”

Section: Introductionmentioning

confidence: 99%

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An accurate identification method based on double weighting for inertial parameters of robot payloads

Fan

Fang

et al. 2022

Robotica

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The inertial parameters of payloads attached to the end effector of robots benefit to several robotics applications, such as the model-based control, the task optimization, and so on. These applications require the accurate estimation of the inertial parameters. In the existing payload estimation approaches, however, the data weighting technique, which can reduce the adverse effects of outliers and significantly improve the final results, has not been applied yet. In this article, an accurate identification method based on double weighting for inertial parameters of robot payloads is proposed. In order to obtain the weighting matrices, a modified dynamic parameter identification method with two loops is firstly proposed. Then, based on the identified results of dynamic parameters, a payload identification model based on double weighting is constructed. In addition, the variations of both nonlinear friction parameters and linear friction parameters caused by the payload are considered in this model. Finally, experimental comparisons between our method and another four methods are conducted. The results confirm that our method shows the best performance, especially on improving the identification accuracy of mass and center of mass of the payload.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An accurate identification method based on double weighting for inertial parameters of robot payloads

Fan

Fang

et al. 2022

Robotica

View full text Add to dashboard Cite

show abstract

“…where the R 6 ; R 15 ; R 24 ; R 3 represent the wheel number of 6, 1 and 5, 2 and 4 and 3, respectively. The kinematic model of BIT-6NAZA mobile robot [21] can be addressed as follows:…”

Section: Tracking Controller Developmentmentioning

confidence: 99%

Human–robot skill transmission for mobile robot via learning by demonstration

Wang

et al. 2021

Neural Comput & Applic

Self Cite

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This paper proposed a skill transmission technique for the mobile robot via learning by demonstration. When the material is transported to the designated location, the robot can show the human-like capabilities: autonomous tracking target. In this case, a skill transmission framework is designed, which the Kinect sensor is utilized to distinguish human activity recognition to create a planned path. Moreover, the dynamic movement primitive method is implemented to represent the teaching data, and the Gaussian mixture regression is utilized to encode the learning trajectory. Furthermore, in order to realize the accurate position control of trajectory tracking, a model predictive tracking control is investigated, where the recurrent neural network is used to eliminate the uncertain interaction. Finally, some experimental tasks using the mobile robot (BIT-6NAZA) are carried out to demonstrate the effectiveness of the developed techniques in real-world scenarios.

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“…Unmanned mobile robot has become a hot topic (Li et al , 2021a, 2022; Yang et al , 2021; Li et al , 2021c, 2021d; Huang et al , 2021; Gao et al , 2021a, 2021b, 2022). Lane detection, as the important content, mainly consists of preprocessing, feature extraction and parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Towards extreme learning machine framework for lane detection on unmanned mobile robot

Dai

Wang

et al. 2022

Self Cite

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Purpose This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge. Design/methodology/approach A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed. Findings Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability. Originality/value This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

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Iterative learning control for a distributed cloud robot with payload delivery

Cited by 34 publications

References 46 publications

An accurate identification method based on double weighting for inertial parameters of robot payloads

An accurate identification method based on double weighting for inertial parameters of robot payloads

Human–robot skill transmission for mobile robot via learning by demonstration

Towards extreme learning machine framework for lane detection on unmanned mobile robot

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