Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

Li, Tzuu-Hseng S.; Kuo, Ping‐Huan; Ho, Ya Fang; Liou, Guan Hong

doi:10.1109/access.2019.2939050

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…In this section, we have designed comparisons of MPSPSO-ST with several classic improved PSO variants (chaos particle swarm optimization (CPSO) [53] particle swarm optimization with the nonlinear dynamic acceleration coefficients (PSO-NDAC) [32] AIWCPSO [54]) and other well-known classic optimization algorithms (differential evolution algorithm (DE), moth-flame optimization algorithm (MFO) and sine cosine algorithm (SCA)). CPSO realizes chaotic searching on the current global best individual using chaos mechanism.…”

Section: Comparison Of Mpspso-st With Cpso Pso-ndac Aiwcpso De Mfo An...mentioning

confidence: 99%

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Fang

2020

Symmetry

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As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.

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Section: Comparison Of Mpspso-st With Cpso Pso-ndac Aiwcpso De Mfo An...mentioning

confidence: 99%

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Fang

2020

Symmetry

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“…Then, we are going to generate a set of motor angles. In this paper, we used the integrated adaptive constricted particle swarm optimization (PSO-IAC) algorithm [36], [37] to generate the motor angles instead of the inverse kinematics method. Though the inverse kinematics is the fastest and easiest method, the generated angles are very different from the original imitated angles, thus violating the primary propose of this motion imitating system.…”

Section: Self-collision Avoidance Systemmentioning

confidence: 99%

Motion Imitation and Augmentation System for a Six Degrees of Freedom Dual-Arm Robot

Liu

Liang

et al. 2019

IEEE Access

Self Cite

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A motion imitation and augmentation system comprising an imitation system, a self-collision avoidance system, and a motion augmented system was proposed in this study. The imitation system captures the human skeleton by using an RGB-D camera and a method involving forward kinematics and the space vector method was developed and integrated to map the human joint angles to the motor angles of the robot's arm. To improve safety of the system, a self-collision avoidance system was incorporated so that the various parts of the robot do not collide with each other while imitating human motions. The imitated motion was recorded and stored in a motion dataset as a reference motion. To address different situations and objects, the reference motion was adjusted using the motion augmented system in which both the adjusted trajectory and its corresponding motor angles were generated using the integrated adaptive constricted particle swarm optimization algorithm. So that, the adjusted motion can adapt to the real situation while maintaining similarity with the reference motion. Experiments conducted in practical situations demonstrated the efficiency of the motion imitation and augmentation system. The robot can mimic a reference motion by using its vision and can generate a suitable motion based on the imitated motion for coping with different objects and situations.

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“…Similarly, machine learning can also detect abnormalities and falling in the human body, mainly by collecting human electromyographic signals, acceleration signals, and video signals. For badminton, table tennis, tennis, football, and other complex activities, the wearable acceleration sensor can be adopted to collect the data, and then the machine learning and other related algorithms are adopted for recognition and classification (Li et al, 2019b). Machine learning and AI technology therefore have broad applicational prospects in the field of sports.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

2021

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This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets, after the accuracy of the robot is analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.

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Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

Cited by 13 publications

References 33 publications

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

Motion Imitation and Augmentation System for a Six Degrees of Freedom Dual-Arm Robot

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

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