Positioning error compensation of an industrial robot using neural networks and experimental study

Li, Bo; Tian, Wei; Zhang, Chufan; Hua, Fangfang; Cui, Guangyu; Li, Yufei

doi:10.1016/j.cja.2021.03.027

Cited by 88 publications

(24 citation statements)

References 18 publications

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“…An artificial neural network (ANN) is an automatic learning and processing paradigm inspired by the functioning of the human nervous system [58], [59], [65]- [67], [74]. A neural network is composed of a set of neurons interconnected by links, where each neuron takes as inputs the outputs of the preceding neurons, multiplies each of these inputs by a weight and, by means of an activation function, calculates an output.…”

Section: Methodsmentioning

confidence: 99%

“…However, in the field of modeling systems for mineral processing (grinding, classifying and concentrating), artificial neural networks are relatively recent, but their use is increasing in this type of systems due to the efficiency of the results that they can generate, avoiding the implementation of complex calculations with better performance [16], [24]. Currently, the intelligence systems by means of artificial neural networks can be summarized into four structures: i) supervised learning: the neural network learns a set of inputs and the desired outputs to solve the problem [53]- [56], ii) direct inverse learning: the neural network learns from the feedback of a system, so that, when the signal is obtained, it determines the parameters to be performed [52], [57]- [60], iii) utility backpropagation: this structure optimizes the mathematical equation that represents the system, where its main disadvantage is that it requires a model of the system to be analyzed [61]- [65] and iv) adaptive critical learning: similar to the utility backpropagation structure, but without the need for a model of the plant [66]- [68]. Although this type of structures are present and well accepted in different industrial processes, it is evident that in mineral processing applications and especially in the prediction of variables of interest such as mineral recovery, the existing studies of this type of design are based on simulations, this research being a starting point for the implementation of intelligent systems in gravimetric concentration equipment where experimental data obtained from a pilot scale jig is worked on.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a twomonth field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage.Results showed that the model (R 2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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“…This simple algorithm can model robots with different configurations and purposes. Besides CHARMIE, studied in the following sections, Figure 3 shows three examples of kinematic models obtained using this method: (a) a mobile quadruped robot similar to SPOT from Boston Dynamics [26]; (b) a fixed serial manipulator similar to KUKA KR 500-3 [27]; and (c) a mobile hexapod similar to the one presented in [28]. Similar to the quadruped robot, after interaction with the floor is defined, this model can be used to study locomotion.…”

Section: Recursive Algorithm For the Computation Of Forward Kinematicsmentioning

confidence: 99%

A Recursive Algorithm for the Forward Kinematic Analysis of Robotic Systems Using Euler Angles

Gonçalves

Ribeiro

et al. 2022

Robotics

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Forward kinematics is one of the main research fields in robotics, where the goal is to obtain the position of a robot’s end-effector from its joint parameters. This work presents a method for achieving this using a recursive algorithm that builds a 3D computational model from the configuration of a robotic system. The orientation of the robot’s links is determined from the joint angles using Euler Angles and rotation matrices. Kinematic links are modeled sequentially, the properties of each link are defined by its geometry, the geometry of its predecessor in the kinematic chain, and the configuration of the joint between them. This makes this method ideal for tackling serial kinematic chains. The proposed method is advantageous due to its theoretical increase in computational efficiency, ease of implementation, and simple interpretation of the geometric operations. This method is tested and validated by modeling a human-inspired robotic mobile manipulator (CHARMIE) in Python.

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“…As a strategic industry, the aviation industry plays an important role in guaranteeing the economy, technology and national defense construction (Li et al, 2022). As for aircraft fabrication, the assembly workload generally accounts for 50-70% of that of the whole manufacturing, which renders it the most important aspect (P erez et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Human-robot collaborative assembly of movable airfoils using adaptive admittance control

Shen¹,

Li²,

Tian³

et al. 2022

JIMSE

Self Cite

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PurposeWith the increasing requirements for intelligence in the field of aviation manufacturing, manual assembly can hardly adapt to the trend of future production. The purpose of this study is to realize the semi-automatic assembly of the movable airfoil by proposing a human-robot collaborative assembly strategy based on adaptive admittance control.Design/methodology/approachA logical judgment system for operating intentions is introduced in terms of different situations of the movements; hence, a human cognition-based adaptive admittance control method is developed to curb the damage of inertia; then virtual limit walls are raised on the periphery of the control model to ensure safety; finally, simulated and experimental comparisons with other admittance control methods are conducted to validate the proposed method.FindingsThe proposed method can save at least 28.8% of the time in the stopping phase which effectively compensates for inertia during the assembly process and has high robustness concerning data disturbances.Originality/valueDue to the human-robot collaboration to achieve compliant assembly of movable airfoils can preserve human subjectivity while overcoming the physical limits of humans, which is of great significance to the investigation of intelligent aircraft assembly, the proposed method that reflects the user's naturalness and intuitiveness can not only enhance the stability and the flexibility of the manipulation, but also contribute to applications of industrial robots in the field of human-robot collaboration.

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Positioning error compensation of an industrial robot using neural networks and experimental study

Cited by 88 publications

References 18 publications

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

A Recursive Algorithm for the Forward Kinematic Analysis of Robotic Systems Using Euler Angles

Human-robot collaborative assembly of movable airfoils using adaptive admittance control

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