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1 – 2 of 2Yimei Chen, Huanhuan Cheng and Baoquan Li
The purpose of this study is to propose a path-planning strategy based on the velocity-virtual spring method to realize collision-free tasks in dynamic environments and further…
Abstract
Purpose
The purpose of this study is to propose a path-planning strategy based on the velocity-virtual spring method to realize collision-free tasks in dynamic environments and further improve the effect.
Design/methodology/approach
By considering factors such as the relative velocity and direction of dynamic obstacles, the repulsive force of the robot is improved, thereby enhancing the adaptability of the strategy and achieving flexible and effective avoidance against dynamic obstacles. The attraction formula has been designed to allow the robot to have better smooth changes and higher gradients near the target, helping robots better reach the target and follow formations. Moreover, to meet the demands of the various stages during the driving process, the null space behavioral control is used to solve multi-task conflict problems and strengthen formation coordination and control.
Findings
Comparison of the planning path and formation effects through simulation and physical experiments, the results of this study show that the algorithm proposed can successfully maintain formation stability and plan smooth and safe paths in static or dynamic environments.
Originality/value
This paper proposes a path-planning strategy based on the velocity-virtual spring method to plan collision-free paths for formation in dynamic environments.
Details
Keywords
Xiaobo Tang, Heshen Zhou and Shixuan Li
Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly…
Abstract
Purpose
Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.
Design/methodology/approach
This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.
Findings
Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.
Originality/value
Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.
Details