Path planning for coal mine robot via improved ant colony optimization algorithm

Song, Baoye; Miao, Huimin; Xu, Lin

doi:10.1080/21642583.2021.1901158

Cited by 39 publications

(12 citation statements)

References 13 publications

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“…e formula represents the distance from fence i to i. Suppose the ant colony is initialized [17] pheromone ℓ ii (d) � c. After n times, the ant completes a cycle, and the amount of pheromone on each path will change due to the passage of the ant and the change of time. e change rules are as follows:…”

Section: Revised Ant Colonies Computer Systemmentioning

confidence: 99%

Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization Algorithm

Jiao

2022

Security and Communication Networks

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The study of mobile robots, which began in the late 1960s, is the most dramatic development in human history in the twentieth century, and the invention has undergone radical changes in just over 50 years. The robot body is developing in the direction of flexibility and miniaturization. This is because the robot application is mostly oriented to the family and service industries, and it needs to adapt to a more complex environment. This manuscript aims to improve further the ant colony optimization algorithm by using rough set theory to improve the convergence speed and accuracy of the algorithm in robot path planning on the basis of an in-depth diagnosis on the shortcomings and its causes of development of the ant colony optimization algorithm. It overcomes the drawbacks of the algorithm that easily get trapped in partial optimality solution, the search time is much slower and the search effect is not good. In this paper, the CMA-ES algorithm, the modified ant colony, and the BK method are proposed, which have high theoretical value and exploration significance. In addition, simulation experiments are conducted to obtain the stage results on the basis of artificial information. The results of the present paper indicate the GWO algorithm performs more stable in the optimization results of each experiment when there are 14 robots and the communication range is 1.6, compared with the PSO algorithm and BA algorithm.

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Section: Revised Ant Colonies Computer Systemmentioning

confidence: 99%

Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization Algorithm

Jiao

2022

Security and Communication Networks

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“…[1] Dataset Ⅳa [45] This dataset consists of EEG signals of three MI tasks (left hand, right hand, and right foot) recorded from 118 EEG channels, with only cues of the right hand and foot tasks provided for competition purpose. Five healthy subjects (aa, al, av, aw, and ay) participate in the experiments, and cues of 280 trials are provided for each subject.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…More details of this dataset can be found on the competition website (http://www.bbci.de/competition/iii/desc_ IVa.html). [2] Dataset Ⅲa [45] This dataset is recorded over 60 EEG channels at 250Hz from three subjects (k3, k6, and l1). Every subject is instructed to imagine four kinds of movement, namely, MI tasks of the left hand, right hand, tongue, and foot.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Yang

Huang

2022

IJNDI

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Survey/review study Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,* 1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States * Correspondence: Mengjie.Huang@liverpool.ac.uk Received: 12 October 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.

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“…It is worth mentioning that evolutionary computation (EC) has been widely used to solve various optimization problems [39], [50]- [52]. Among the EC algorithms, the particle swarm optimization (PSO) algorithm is a population-based one, which is inspired by the mimics of social interactions, e.g., birds flocking and fish schooling [55], [57].…”

Section: Introductionmentioning

confidence: 99%

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

Fang

Wang

Liu

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data. Note to Practitioners-Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters.A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed

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Path planning for coal mine robot via improved ant colony optimization algorithm

Cited by 39 publications

References 13 publications

Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization Algorithm

Simulation Research on the Palm Mechanism of Volleyball Robot Based on Artificial Intelligence and Ant Colony Optimization Algorithm

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

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