This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the class...This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.展开更多
To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform in...To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
Recent developments in Unmanned Aerial Vehicles(UAVs)and their applications in various subjects are of interest to polar communities.Due to the harsh climate and dangerous environment,these regions pose challenges for...Recent developments in Unmanned Aerial Vehicles(UAVs)and their applications in various subjects are of interest to polar communities.Due to the harsh climate and dangerous environment,these regions pose challenges for the expedition teams.Several countries have tested the UAV technology to support Antarctic research and logistics.In this trend paper,we provide insightful reviews and discussions on such a prospective topic.Based on a comprehensive literature survey,we firstly summarize the key research progress of UAV in Antarctic studies.Then the examples of risk scenarios during the field exploration are given,after which several promising applications of the UAVs in safety guarantee are illustrated.In particular,we present a case of site-selection for the Chinese first ice sheet airfield,using the data collected in the 34th Chinese National Antarctic Research Expedition(CHINARE).In the end,we highlight the unique value of the UAVs in the popularization of polar science before concluding the advantages and limitations.Considering their excellent performance,we expect more innovations for UAV’s applications in the following Antarctic expeditions.展开更多
This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles （mini-UAVs） to a mission area. The algorithm is based on...This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles （mini-UAVs） to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense （SEAD） problem is presented to illustrate the proposed algorithm.展开更多
面向低空空域管理与城市配送典型场景,构建了一体化仿真实验平台。涵盖了空域划设、路径规划以及可视化展示等核心模块,融合空管理论、优化算法与结果可视化技术,实现了对无人机运行航路规划的仿真。该实验设计了场景导入、环境建模、...面向低空空域管理与城市配送典型场景,构建了一体化仿真实验平台。涵盖了空域划设、路径规划以及可视化展示等核心模块,融合空管理论、优化算法与结果可视化技术,实现了对无人机运行航路规划的仿真。该实验设计了场景导入、环境建模、约束分析、模型设计以及算法实现等环节,通过城市物流无人机路径规划案例进行示范教学,优化航路长度、飞行时间及能量消耗等指标,提升学生的技术素养与实践能力。该实验设计适用于无人机空中交通运输、低空经济管理等课程,有助于高校虚拟仿真实验室建设与创新型城市空中交通(urban air mobility,UAM)人才培养。展开更多
文摘This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.
文摘To fulfill the training requirements for the daily operations of multirotor unmanned aerial vehicles(UAVs)clusters,a UAV cluster collaborative task integrated simulation platform(UAV-TISP)was developed.The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios.It features modules for collaborative task planning,UAV cluster simulations,and tactical monitoring.The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms.UAV-TISP supports both individual UAV and swarm operations,incorporating high-fidelity flight dynamics,real-time communication via user datagram protocol(UDP),and collision avoidance strategies.Utilizing the OSGEarth engine,it enables dynamic 3D environment visualization and scenario customization.Three key task scenarios-route flight,formation reconstruction,and formation transformation-were tested to validate the platform’s efficacy.Results demonstrated robust formation maintenance,adaptive collision avoidance,and seamless task execution.Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP,highlighting its superior accuracy in simulating real-world flight dynamics.Future work will focus on enhancing scalability for diverse UAV models,optimizing swarm networking under communication constraints,and expanding mission scenarios.UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
基金the National Natural Science Foundation of China(Grant nos.41830536,41676176 and 41676182)the Chinese Polar Environment Comprehensive Investigation and Assessment ProgramTeng Li is also funded by the UK-China Joint Research and Innovation Partnership Fund PhD Placement Program.
文摘Recent developments in Unmanned Aerial Vehicles(UAVs)and their applications in various subjects are of interest to polar communities.Due to the harsh climate and dangerous environment,these regions pose challenges for the expedition teams.Several countries have tested the UAV technology to support Antarctic research and logistics.In this trend paper,we provide insightful reviews and discussions on such a prospective topic.Based on a comprehensive literature survey,we firstly summarize the key research progress of UAV in Antarctic studies.Then the examples of risk scenarios during the field exploration are given,after which several promising applications of the UAVs in safety guarantee are illustrated.In particular,we present a case of site-selection for the Chinese first ice sheet airfield,using the data collected in the 34th Chinese National Antarctic Research Expedition(CHINARE).In the end,we highlight the unique value of the UAVs in the popularization of polar science before concluding the advantages and limitations.Considering their excellent performance,we expect more innovations for UAV’s applications in the following Antarctic expeditions.
基金supported by the National Natural Science Foundation of China(60774064)the Aerospace Science Foundation (20085153015)
文摘This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles （mini-UAVs） to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense （SEAD） problem is presented to illustrate the proposed algorithm.
文摘面向低空空域管理与城市配送典型场景,构建了一体化仿真实验平台。涵盖了空域划设、路径规划以及可视化展示等核心模块,融合空管理论、优化算法与结果可视化技术,实现了对无人机运行航路规划的仿真。该实验设计了场景导入、环境建模、约束分析、模型设计以及算法实现等环节,通过城市物流无人机路径规划案例进行示范教学,优化航路长度、飞行时间及能量消耗等指标,提升学生的技术素养与实践能力。该实验设计适用于无人机空中交通运输、低空经济管理等课程,有助于高校虚拟仿真实验室建设与创新型城市空中交通(urban air mobility,UAM)人才培养。
