A task level fusion autonomous switching mechanism.

Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

Isolation of Main Pathogens Causing Postharvest Disease in Fresh Codonopsis pilosula during Different Storage Stages and Ozone Control against Disease and Mycotoxin Accumulation.

Codonopsis pilosula is an important Chinese herbal medicine. However, fresh C. pilosula is prone to decay during storage due to microorganism infections, seriously affecting the medicinal value and even causing mycotoxin accumulation. Therefore, it is necessary to study the pathogens present and develop efficient control strategies to mitigate their detrimental effects on the herbs during storage. In this study, fresh C. pilosula was collected from Min County in Gansu Province, China. The natural disease symptoms were observed during different storage stages, and the pathogens causing C. pilosula postharvest decay were isolated from the infected fresh C. pilosula. Morphological and molecular identification were performed, and pathogenicity was tested using Koch's postulates. In addition, the control of ozone was examined against the isolates and mycotoxin accumulation. The results indicated that the naturally occurring symptom increased progressively with the extension of storage time. The mucor rot caused by Mucor was first observed on day 7, followed by root rot caused by Fusarium on day 14. Blue mold disease caused by Penicillum expansum was detected as the most serious postharvest disease on day 28. Pink rot disease caused by Trichothecium roseum was observed on day 56. Moreover, ozone treatment significantly decreased the development of postharvest disease and inhibited the accumulations of patulin, deoxynivalenol, 15-Acetyl-deoxynivalenol, and HT-2 toxin.