Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Boantă, Cătălin; Brişan, Cornel

doi:10.3390/s22218356

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Future work will analyze the application of this non-parametric procedure in several three-wheeled omnidirectional mobile robots and the application of alternative methods based on artificial neural networks [37] to estimate the kinematics of an omnidirectional mobile robot.…”

Section: Discussionmentioning

confidence: 99%

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

et al. 2023

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Odometry is a computation method that provides a periodic estimation of the relative displacements performed by a mobile robot based on its inverse kinematic matrix, its previous orientation and position, and the estimation of the angular rotational velocity of its driving wheels. Odometry is cumulatively updated from tens to hundreds of times per second, so any inaccuracy in the definition of the inverse kinematic matrix of a robot leads to systematic trajectory errors. This paper proposes a non-parametric calibration of the inverse kinematic (IK) matrix of a three-wheeled omnidirectional mobile robot based on the use of genetic algorithms (GA) to minimize the positioning error registered in a set of calibration trajectories. The application of this non-parametric procedure has provided an average improvement of 82% in the estimation of the final position and orientation of the mobile robot. This is similar to the improvement achieved with analogous parametric methods. The advantage of this non-parametric approach is that it covers a larger search space because it eliminates the need to define feasible physical limits to the search performed to calibrate the inverse kinematic matrix of the mobile robot.

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Section: Discussionmentioning

confidence: 99%

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

et al. 2023

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“…Li et al [28] employed an improved Monte Carlo method to generate clearer boundaries for the dynamic workspace of a multi-robot collaborative towing system. Boanta and Bris , an [29] combined robot kinematics with feedforward neural networks to estimate the workspace volume of a robot and solve for the Cartesian coordinates of the robot's end-effector in a Cartesian space. Xu et al [30] divided the Monte Carlo algorithm into two stages, namely, subspace generation and subspace expansion, to generate the workspace of a robot.…”

Section: Introductionmentioning

confidence: 99%

On the Relative Kinematics and Control of Dual-Arm Cutting Robots for a Coal Mine

Liu,

Zhou,

Qiao

et al. 2024

Actuators

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There is an unbalanced problem in the traditional laneway excavation process for coal mining because the laneway excavation and support are at the same position in space but they are separated in time, consequently leading to problems of low efficiency in laneway excavation. To overcome these problems, an advanced dual-arm tunneling robotic system for a coal mine is developed that can achieve the synchronous operation of excavation and the permanent support of laneways to efficiently complete excavation tasks for large-sized cross-section laneways. A dual-arm cutting robot (DACR) has an important influence on the forming quality and excavation efficiency of large-sized cross-section laneways. As a result, the relative kinematics, workspace, and control of dual-arm cutting robots are investigated in this research. First, a relative kinematic model of the DACR is established, and a closed-loop control strategy for the robot is proposed based on the relative kinematics. Second, an associated workspace (AW) for the DACR is presented and generated, which can provide a reference for the cutting trajectory planning of a DACR. Finally, the relative kinematics, closed-loop kinematic controller, and associated workspace generation algorithm are verified through simulation results.

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“…Nowadays, major diagnosis methods of robot faults ,there are expert system diagnosis methods, artificial neural network methods and mathematical model-based methods [22][23][24]. The development of a new expert system for diagnosing marine diesel engines based on real-time diagnostic parameters proposed by Gharib [25] , and tEstimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks proposed by Boanta apply artificial neural network to evaluate the workspace of robots [26] .…”

Section: Introductionmentioning

confidence: 99%

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

Xie,

Zhao,

Zhu

2024

Preprint

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To address difficult recognition of vibration signals of heavy-load robots, a new median neural network pattern recognition method based on artificial neural network was proposed. This method builds the pattern recognition network model by using the median neurons and particle swarm optimization algorithm. In other words, the fault modes of heavy-load industrial robots are recognized quickly and accurately by using median and multiplication function as the aggregate function, and combining with the particle swarm backpropagation algorithm under the premise that there’s abnormal or fault eigenvalues in the network input mode. According to the simulation analysis and case verification, the proposed median neural network method can recognize fault modes of heavy-load robots well and it still and a smaller one recognition fault rate more traditional algorithms In below the complicated severe environment with noises. It shows remarkable recognition accuracy as well as relatively high compatibility and stability. This method not only has high theoretical value in pattern recognition of heavy-duty robots, but also possesses very important engineering practice significance in fault pattern recognition of rotary machines like ventilator and gas blower.The proposed method not only has high theoretical value in pattern recognition of heavy-duty robots, but there are also important,it has practical significance in engineering and other fields fault pattern recognition of rotating machinery such as ventilators and gas blowers.

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Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks

Cited by 7 publications

References 26 publications

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

On the Relative Kinematics and Control of Dual-Arm Cutting Robots for a Coal Mine

Research on Fault pattern Recognition of High-speed and Heavy-load Robots based on Median Neural Network

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