Programming by Demonstration Using Two-Step Optimization for Industrial Robot.

Ostanin, Mikhail; Popov, Dmitry; Klimchik, Alexandr

doi:10.1016/j.ifacol.2018.08.237

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…This leads to the achievement of an optimal programmed trajectory in time, by increasing the speed of movement along the degrees of MR mobility. A method for developing programmed trajectories, consisting of two stages, was proposed in [19]. At the first stage, the programmed trajectory is optimized using static methods.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In the case of production of commercial magnesium, all parameters of movement along the degrees of MR mobility are specified and limited by the operating parameters of the casting conveyor. This leads to unambiguous solutions, then it is difficult to pose the problem of optimizing programmed trajectories using the methods proposed in [17][18][19].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Development of programmed trajectories based on the mobility degrees of manipulation robot with a spherical coordinate system for removing oxide film in the production of commercial magnesium

Beisembayev,

Yerbossynova,

Pavlenko

et al. 2024

EEJET

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The object of this study is to robotize the technological operation of removing the oxide film from the surface of a magnesium melt poured into continuously moving molds of a casting conveyor for the production of commercial magnesium. To robotize this technological operation, it is proposed to use a two-armed manipulation robot with a spherical coordinate system, which has six degrees of mobility. Software trajectories have been developed according to the degrees of mobility of the manipulation robot in terms of position, speed, and acceleration to perform the technological operation of removing the oxide film from the surface of the magnesium melt poured into the moving molds of the foundry conveyor. Programmed trajectories are described by quadratic polynomials that satisfy restrictions on the values of the generalized coordinate, velocity, and acceleration. These limitations are determined by the design features and energy capabilities of the degrees of mobility drives of the manipulation robot. Programmed trajectories along the first and second degrees of freedom compensate for the continuous movement of the molds of the foundry conveyor. Programmed trajectories along the third and fourth degrees of mobility enable the collection of the oxide film from the surface of the magnesium melt. Programmed trajectories along the fifth and sixth degrees of freedom enable the discharge of the collected oxide film into a special container. The reliability of the developed programmed trajectories is confirmed by the simulation results using MATLAB version R2015b. Based on the results, a cyclogram for controlling a manipulation robot has been constructed to perform the technological operation of removing the oxide film in the production of commercial magnesium. The results could be used in the robotization of technological processes for removing the oxide film in the production of commercial magnesium or similar foundries

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of programmed trajectories based on the mobility degrees of manipulation robot with a spherical coordinate system for removing oxide film in the production of commercial magnesium

Beisembayev,

Yerbossynova,

Pavlenko

et al. 2024

EEJET

View full text Add to dashboard Cite

show abstract

“…In this approach, the human inconsistency in repeating the same desired trajectory is not treated as a noise to be canceled but is considered a measure of accuracy required by the task. A similar approach is proposed in [16], where several trajectories obtained by observing a human operator that replicates a given task are discretized in space and then condensed in a sequence of pairs composed by the average point and the standard deviation. On the basis of these pairs, a feasible space is constructed, and then, different trajectories for a robotic system are computed optimizing criteria such as the total length, the execution time, or the energy consumption.…”

Section: A Related Workmentioning

confidence: 99%

Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

et al. 2023

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Trajectory learning is one of the key components of robot Programming by Demonstration approaches, which in many cases, especially in industrial practice, aim at defining complex manipulation patterns. In order to enhance these methods, which are generally based on a physical interaction between the user and the robot, guided along the desired path, an additional input channel is considered in this article. The hand stiffness, that the operator continuously modulates during the demonstration, is estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. Then, a constrained optimization problem is built (and solved) in the framework of smoothing B-splines to obtain a minimum curvature trajectory approximating, in this manner, the taught path within the precision imposed by the user. Experimental tests in different applicative scenarios, involving both position and orientation, prove the benefits of the proposed approach in terms of the intuitiveness of the programming procedure for the human operator and characteristics of the final motion.

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“…A potential method is to use numerous demonstration samples to improve the performance [13], which can be used in the overall assembly task and works for specific segments such as grasping tasks [14]. Also, minimizing energy, time consumption, and shortest distance are other ways to define optimal demonstration performance [15].  Learning algorithm Algorithms and optimization also need to be considered in robotics to support programming by demonstration.…”

Section: Introduction mentioning

confidence: 99%

Strategy with machine learning models for precise assembly using programming by demonstration

Bai

Hsieh

2023

Preprint

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Programming by Demonstration(PBD) applies in the industry as a method of human-robot collaboration for assembly, such as placing, inserting, and screwing; however, PBD has not received much attention in commercial electronic product assembly. There is little work on integrating a strategy to optimize trajectory planning, which is an initial step of PBD. Most recent related works improving PBD performance focus on taking care of numerous demonstration samples from motion-sensing devices under time consumption, distance, and other related criteria. However, it is necessary to integrate a strategy to optimize the performance of precise assembly tasks. We proposed a framework with two custom algorithms to pre-process and classify contactless demonstration performance in this research work. It verified that these algorithms could more effectively generate optimal motion paths in criteria of distance, smoothness, and trajectory variance than canonical methods. Machine learning methods, including the Convolutional neural network(CNN), Artificial Neural Network(ANN), and Support Vector Machine(SVM), are feasible to predict the further performance that the best motion path with accuracy ranging from 80% to 85% accuracy. Also, CNN performs better than ANN and SVM. Among CNN methods, the DarkNet yields the highest accuracy rate of 85%. Future work includes the hybrid CNN/ANN algorithm, which may yield higher accuracy in prediction. Also, the proposed algorithms may apply to robots with dual assembly arms which mimic human arms.

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Programming by Demonstration Using Two-Step Optimization for Industrial Robot.

Cited by 11 publications

References 15 publications

Development of programmed trajectories based on the mobility degrees of manipulation robot with a spherical coordinate system for removing oxide film in the production of commercial magnesium

Development of programmed trajectories based on the mobility degrees of manipulation robot with a spherical coordinate system for removing oxide film in the production of commercial magnesium

Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation

Strategy with machine learning models for precise assembly using programming by demonstration

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