Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited reso...Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.展开更多
This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is ...This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.展开更多
There are regular problems of musty odor in the Huangpu River,a major source of drinking water for Shanghai,China.In this study,the musty odor and its main causative compounds in the Huangpu River source water were co...There are regular problems of musty odor in the Huangpu River,a major source of drinking water for Shanghai,China.In this study,the musty odor and its main causative compounds in the Huangpu River source water were confirmed through a yearly investigation using flavor profile analysis combined with HSPME-GC-MS analysis.The investigation showed that 2-methylisoborneol （2-MIB） with a concentration level between 28.6 and 71.0 ng/L was responsible for the musty odor in summer from July to September.Microscopic observation confirmed with the cloning results showed that Phormidium spp.,which accounted for 80%-95% of the algal cell density,was the microorganisms responsible for the production of 2-MIB and the estimated 2-MIB yield was 0.022 pg/cell.Results from a wide-area sampling campaign in the Huangpu River watershed showed that,other than the large tributaries receiving water from Tai Lake,several small creeks close to the intake may have contributed most of the 2-MIB and the Phormidium spp.to the Huangpu River source water.This study provides methodology for the investigation of odor causing compounds and microorganisms in river-type source water,and the result will be useful for water quality control in both source water and drinking water.展开更多
基金supported by National Natural Science Foundation of China(No.62072436 and No.62202449)National Key Research and Development Program of China(2021YFB2900102).
文摘Benefitting from UAVs’characteristics of flexible deployment and controllable movement in 3D space,odor source localization with multiple UAVs has been a hot research area in recent years.Considering the limited resources and insufficient battery capacities of UAVs,it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states.To this end,we propose a multi-UAV collaboration based odor source localization(MUC-OSL)method,where source estimation and UAV navigation are iteratively performed,aiming to accelerate the searching process and reduce the resource consumption of UAVs.Specifically,in the source estimation phase,we present a collaborative particle filter algorithm on the basis of UAVs’cognitive difference and collaborative information to improve source estimation accuracy.In the following navigation phase,an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV.The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate,and effectively reduces the resource consumption of UAVs.
基金supported by National Natural Science Foundation of China (No. 60675043)Natural Science Foundation of Zhejiang Province of China (No. Y1090426, No. Y1090956)Technical Project of Zhejiang Province of China (No. 2009C33045)
文摘This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.
基金supported by the National Natural Science Foundation of China(No.50938007)the National Special Funding Project for Water Pollution Control and Management of China(No.2008ZX07421004)
文摘There are regular problems of musty odor in the Huangpu River,a major source of drinking water for Shanghai,China.In this study,the musty odor and its main causative compounds in the Huangpu River source water were confirmed through a yearly investigation using flavor profile analysis combined with HSPME-GC-MS analysis.The investigation showed that 2-methylisoborneol （2-MIB） with a concentration level between 28.6 and 71.0 ng/L was responsible for the musty odor in summer from July to September.Microscopic observation confirmed with the cloning results showed that Phormidium spp.,which accounted for 80%-95% of the algal cell density,was the microorganisms responsible for the production of 2-MIB and the estimated 2-MIB yield was 0.022 pg/cell.Results from a wide-area sampling campaign in the Huangpu River watershed showed that,other than the large tributaries receiving water from Tai Lake,several small creeks close to the intake may have contributed most of the 2-MIB and the Phormidium spp.to the Huangpu River source water.This study provides methodology for the investigation of odor causing compounds and microorganisms in river-type source water,and the result will be useful for water quality control in both source water and drinking water.
