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The purpose of this paper is to construct a grey clustering evaluation model based on center-point mixed possibility function and to evaluate the tourism development potential of tea intangible cultural heritage. The research results provide a certain reference for the tourism development department and related researchers who are engaged in the tourism development of intangible cultural heritage.

The study uses literature research, questionnaire investigation, expert interviews and factor analysis to determine the evaluation index system of tourism development potential of tea intangible cultural heritage and applies analytic hierarchy process (AHP) to determine the weight of each criteria. Then, according to the grey clustering evaluation theory and two-stage decision model, a grey clustering evaluation model is constructed to assess the tourism development potential of tea intangible cultural heritage. Finally, a new model is employed to evaluate the tourism development potential of tea intangible cultural heritage in Huangshan city.

The results show that there is a big difference in the tourism development potential of different tea intangible cultural heritages in Huangshan City and it further illustrates the scientificity and rationality of the method proposed in this paper.

The model constructed in the paper can be effectively applied to the evaluation of tourism development potential of tea intangible cultural heritage scientifically and reasonably.

This manuscript not only constructs the evaluation index system of tourism development potential of tea intangible cultural heritage but also creatively applies the grey clustering theory to the evaluation of tourism development potential of tea intangible cultural heritage, which provides a new research idea for the evaluation of tourism development potential of tea intangible cultural heritage.

Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data.

A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm.

The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand.

The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism.

The purpose of this paper is to provide a comprehensive and unified presentation of recent developments in skid-steer wheeled mobile robots (SSWMR) with regard to its control, guidance and navigation for the researchers who wish to study in this field.

Most of the contemporary unmanned ground robot’s locomotion is based upon the wheels. For wheeled mobile robots (WMRs), one of the prominent and widely used driving schemes is skid steering. Because of mechanical simplicity and high maneuverability particularly in outdoor applications, SSWMR has an advantage over its counterparts. Different prospects of SSWMR have been discussed including its design, application, locomotion, control, navigation and guidance. The challenges pertaining to SSWMR have been pointed out in detail, which will seek the attention of the readers, who are interested to explore this area.

Relying on the recent literature on SSWMR, research gaps are identified that should be analyzed for the development of autonomous skid-steer wheeled robots.

An attempt to present a comprehensive review of recent advancements in the field of WMRs and providing references to the most intriguing studies.