RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi, Yonghong; Jiang, Yan

doi:10.1177/1729881420909965

Cited by 5 publications

(4 citation statements)

References 26 publications

(38 reference statements)

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“…They applied the fuzzy control theory to behavior control structures and combined these two path planning algorithms to solve the path planning problem of basketball robots in unknown environments. The results show that the basketball robot can overcome the uncertainty in the environment and effectively achieve good path planning, which verifies the feasibility of the fuzzy control algorithm and the validity and correctness of the path planning strategy (Zhi and Jiang, 2020). Cox et al (2021) built a generic controller for regulating the motion of an inertiadriven jumping robot.…”

Section: Related Research Analysismentioning

confidence: 71%

“…The application of this algorithm can effectively prevent sports injuries in basketball (Xu and Tang, 2021 ). Zhi and Jiang ( 2020 ) proposed a path planning algorithm based on behavioral module control, aiming at problems, such as the strong dependence of traditional algorithms on environmental information, the path planning of basketball robots in unknown environments, and the improvement of autonomous navigation safety. They applied the fuzzy control theory to behavior control structures and combined these two path planning algorithms to solve the path planning problem of basketball robots in unknown environments.…”

Section: Literature Reviewmentioning

confidence: 99%

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Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

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The primary research purpose lies in studying the intelligent detection of movements in basketball training through artificial intelligence (AI) technology. Primarily, the theory of somatosensory gesture recognition is analyzed, which lays a theoretical foundation for research. Then, the collected signal is denoised and normalized to ensure that the obtained signal data will not be distorted. Finally, the four algorithms, decision tree (DT), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), are used to detect the data of athletes' different limb movements and recall. The accuracy of the data is compared and analyzed. Experiments show that the back propagation (BP) ANN algorithm has the best action recognition effect among the four algorithms. In basketball training athletes' upper limb movement detection, the average accuracy rate is close to 93.3%, and the average recall is also immediate to 93.3%. In basketball training athletes' lower limb movement detection, the average accuracy rate is close to 99.4%, and the average recall is immediate to 99.4%. In the detection of movements of upper and lower limbs: the recognition method can efficiently recognize the basketball actions of catching, passing, dribbling, and shooting, the recognition rate is over 95%, and the average accuracy of the four training actions of catching, passing, dribbling, and shooting is close to 98.95%. The intelligent basketball training system studied will help basketball coaches grasp the skilled movements of athletes better to make more efficient training programs and help athletes improve their skill level.

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Section: Related Research Analysismentioning

confidence: 71%

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Technology in Basketball Training Action Recognition

Liang

Xu²,

Kuang³

2022

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…The current path planning algorithms mainly include the colony algorithms (Liu et al, 2019;Ye et al, 2020;Zhang et al, 2020;Zhu et al, 2020), PSO (Krell et al, 2019;Wang Y. B. et al, 2019;Liu X. H. et al, 2021;Song et al, 2021), A * algorithms (Xiong et al, 2020;Tang et al, 2021;Tullu et al, 2021), artificial potential field methods Azmi and Ito, 2020;Song et al, 2020;Yao et al, 2020), genetic algorithms (Hao et al, 2020;Li K. R. et al, 2021;Wen et al, 2021), fuzzy control algorithms (Guo et al, 2020;Zhi and Jiang, 2020), fast marching algorithms (Sun et al, 2021;Wang et al, 2021;Xu et al, 2021), and deep reinforcement learning algorithms (Li L. Y. et al, 2021;Lin et al, 2021;Xie et al, 2021). PSO is an evolutionary computation algorithm that can be used to find the optimal solution through collaboration and information sharing between individuals in the group, as in path planning, the optimal solution is to find the shortest path.…”

Section: Introductionmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

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The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

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“…In robotic and human-interaction systems, it is crucial for a computer to know more information of a customer, such as the identity, gender, age, behaviour, emotion and so on. [1][2][3][4][5] Among these labels/attributes, the identity of a customer might be the most important information for a robot. With this information, the robot could provide customised services thus to improve users' experiences significantly.…”

Section: Introductionmentioning

confidence: 99%

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

Mao

Delei

Chen

et al. 2020

International Journal of Advanced Robotic Systems

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Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

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RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Cited by 5 publications

References 26 publications

Artificial Intelligence Technology in Basketball Training Action Recognition

Artificial Intelligence Technology in Basketball Training Action Recognition

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

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