Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption

Zhang, Xiaoqing; Ming, Zhengfeng

doi:10.3390/app9132770

Cited by 23 publications

(12 citation statements)

References 40 publications

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“…This allows to train a ML model able to estimate these parameters more easily. In robotics, the Lamé curve has been used in smooth trajectory planning for parallel [ 37 ] and delta [ 38 , 39 , 40 ] robots. In this paper, we apply the Lamé curve to compute smooth collision-free toolpaths for an industrial robot arm.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy level of possibly slower, exact algorithmic solutions to the same problems. To this end, the existing simplex architecture for safety assurance in critical systems is extended by an adaptation mechanism, in which one of the redundant controllers (called a high-performance controller) is represented by a trained machine learning model. This model is retrained using field data to reduce its failure rate and redeployed continuously. The proposed adaptive simplex architecture (ASA) is evaluated on the basis of a robot path planning application with dynamic obstacle avoidance in the context of two human-robot collaboration scenarios in manufacturing. The evaluation results indicate that ASA enables a response by the robot in real time when it encounters an obstacle. The solution predicted by the model is economic in terms of path length and smoother than analogous algorithmic solutions. ASA ensures safety by providing an acceptance test, which checks whether the predicted path crosses the obstacle; in which case a suboptimal, yet safe, solution is used.

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Section: Related Workmentioning

confidence: 99%

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Ionescu

2021

Sensors

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“…The moving platform is the execution end of the Par4 parallel robot. The fixed platform mainly includes an installing frame and four servo motors [23]. For Par4 parallel robot, the high-speed pickup is one of the main operations.…”

Section: Expected Trajectorymentioning

confidence: 99%

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Zhang¹,

Ming²

2021

IJCIS

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A dynamic Grey Wolf Optimizer (GWO) is proposed, noted as DGWO2, and a novel dynamic improved GWO algorithm is obtained after transferring the mutation operator and the eliminating-reconstructing mechanism to the DGWO2, noted as DMR-GWO2. A Type-2 fuzzy predictive compensation PID trajectory tracking controller based on the DMR-GWO2 is proposed to attain the tracking targets for the trajectory control of Par4 parallel robot. In the trajectory tracking controller, the DMR-GWO2 is designed for optimizing the Type-2 fuzzy logic controller which is in parallel with the PID controller to speed up the response of the parallel robot and to get the high adaptive capacity of the whole system. One input of the Type-2 fuzzy logic controller is the tracking error, while the other input is the sum of the derivative of the tracking error and the change rate of the expected angle. Finally, the experiments verify the effectiveness of the designed trajectory tracking controller and the tracking errors are small.

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“…The stochastic search strategy has been adopted in GWO. Because of the characteristics of fast convergence rate, simple structure and easy programming, the GWO has been widely applied [8].…”

Section: The Position Vector In the Next Iteration And Parametersmentioning

confidence: 99%

The Training and Application of RBF Neural Network Based on GWO

Zhang¹,

Ming²

2019

dtcse

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To optimize the hidden center matrix, Gaussian RMS width vector and the hidden-output weight matrix of RBF neural network, Grey Wolf Optimizer (GWO) and its several variants have been introduced. With the combination of the three parameters as the position vector of the grey wolf in GWO, selecting half of the average squared error as the optimizing object function, the RBF neural network based on GWO is named as RBF-GWO network. In the processing of training the parameters for RBF-GWO, the difference between the actual output of RBF-GWO network and the desired output value is considered as the guide of updating the position vector for the grey wolf, and in each iteration the optimal parameter values are stored in the position vector of Wolf α which will be returned to the RBF-GWO. The parameter training is completed until the end conditions of the iteration are satisfied. To verify the validity of RBF-GWO network, the continuous function approximation experiment and the chaotic synchronization anti-control experiment have been done in turn. Not only it is proved that the proposed RBF-GWO network is effective, but it is also found that the RBF-GWO network based on the WGWO has a relatively strong adaptive capacity in all experiments from the overall view.

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Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption

Cited by 23 publications

References 40 publications

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

The Training and Application of RBF Neural Network Based on GWO

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