Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

Gholami, Akram; Homayouni, Taymaz; Ehsani, Reza; Sun, Jianqiao

doi:10.3390/robotics10040115

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…The control approach is entirely data-driven, and no prior knowledge of the robot's kinematics is required. To test the suggested controller, several simulations and tests are carried out [13]. Without calibration, the absolute positioning inaccuracy of robots can approach several millimeters.…”

Section: State Of the Art Of Technologymentioning

confidence: 99%

Computer Aided Design of Self-Learning Robotic System Using Imitation Learning

Jadeja

Shafik

Wood

2022

Advances in Transdisciplinary Engineering

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Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Of course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. Further work is still undertaken, and this will focus on learning from more than one expert, optimisation, impact of dynamic manufacturing environment.

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Section: State Of the Art Of Technologymentioning

confidence: 99%

Computer Aided Design of Self-Learning Robotic System Using Imitation Learning

Jadeja

Shafik

Wood

2022

Advances in Transdisciplinary Engineering

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“…A real-time delta robot trajectory control system employing an inverse kinematic controller and neural networks has been presented in [2]. The results demonstrate that joint backlash's detrimental impact on trajectory tracking is diminished and the inaccuracy in trajectory tracking is constrained in the presence of external disturbance.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Shamseldin

2023

Journal of Robotics and Control

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This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m.

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“…Neural networks can be applied to estimate the kinematics and workspace of the Delta robot through a random sampled dataset [16,17]. A real-time neural network-based inverse kinematics control is proposed in [18] by updating the input joint angle with the knowledge of the current rotation angle, current position, and the position for the next step. Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [18] makes use of the neural network to predict the next moving angle of the stepper motor. The setup of the Delta robot in [18] is similar to our setup with different geometries; however, the neural networks in [18] are trained offline with simulation data.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors

Zhao,

Toudeshki,

Ehsani

et al. 2023

Robotics

Self Cite

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The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.

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Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

Cited by 12 publications

References 35 publications

Computer Aided Design of Self-Learning Robotic System Using Imitation Learning

Computer Aided Design of Self-Learning Robotic System Using Imitation Learning

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors

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