An Air Route Planning Model of Unmanned Aerial Vehicles Under Constraints of Ground Safety

With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.

Modeling and control for hydraulic transmission of unmanned ground vehicle

Variable pump driving variable motor(VPDVM) is the future development trend of the hydraulic transmission of an unmanned ground vehicle(UGV).VPDVM is a dual-input single-output nonlinear system with coupling,which is difficult to control.High pressure automatic variables bang-bang(HABB) was proposed to achieve the desired motor speed.First,the VPDVM nonlinear mathematic model was introduced,then linearized by feedback linearization theory,and the zero-dynamic stability was proved.The HABB control algorithm was proposed for VPDVM,in which the variable motor was controlled by high pressure automatic variables(HA) and the variable pump was controlled by bang-bang.Finally,simulation of VPDVM controlled by HABB was developed.Simulation results demonstrate the HABB can implement the desired motor speed rapidly and has strong robustness against the variations of desired motor speed,load and pump speed.

Unmanned Drug Delivery Vehicle for COVID-19 Wards in Hospitals

The prime reason for proposing the work is designing and developing a low-cost guided wireless Unmanned Ground Vehicle(UGV)for use in hospitals for assistance in contactless drug delivery in COVID-19 wards.The Robot is designed as per the requirements and technical specifications required for the healthcare facility.After a detailed survey and tests of various mechanisms for steering and structure of UGV,the best mechanism preferred for steering articulated and for body structure is hexagonal as this approach provides decent performance and stability required to achieve the objective.The UGV has multiple sensors onboard,such as a Camera,GPS module,Hydrogen,and Carbon Gas sensor,Raindrop sensor,and an ultrasonic range finder on UGV for the end-user to understand the circumferential environment and status of UGV.The data and control options are displayed on any phone or computer present in the Wi-Fi zones only if the user login is validated.ESP-32 microcontroller is the prime component utilized to establish reliable wireless communication between the user and UGV.These days,the demand for robot vehicles in hospitals has increased rapidly due to pandemic outbreaks as using this makes a contactless delivery of the medicinal drug.These systems are designed specifically to assist humans in the current situation where life can be at risk for healthcare facilities.In addition,the robot vehicle is suitable for many other applications like supervision,sanitization,carrying medicines and medical equipment for delivery,delivery of food and used dishes,laundry,garbage,laboratory samples,and additional supply.

Heat transfer and temperature evolution in underground mininginduced overburden fracture and ground fissures: Optimal time window of UAV infrared monitoring

Heat transfer and temperature evolution in overburden fracture and ground fissures are one of the essential topics for the identification of ground fissures via unmanned aerial vehicle(UAV) infrared imager. In this study, discrete element software UDEC was employed to investigate the overburden fracture field under different mining conditions. Multiphysics software COMSOL were employed to investigate heat transfer and temperature evolution of overburden fracture and ground fissures under the influence of mining condition, fissure depth, fissure width, and month alternation. The UAV infrared field measurements also provided a calibration for numerical simulation. The results showed that for ground fissures connected to underground goaf(Fissure Ⅰ), the temperature difference increased with larger mining height and shallow buried depth. In addition, Fissure Ⅰ located in the boundary of the goaf have a greater temperature difference and is easier to be identified than fissures located above the mining goaf. For ground fissures having no connection to underground goaf(Fissure Ⅱ), the heat transfer is affected by the internal resistance of the overlying strata fracture when the depth of Fissure Ⅱ is greater than10 m, the temperature of Fissure Ⅱ gradually equals to the ground temperature as the fissures’ depth increases, and the fissures are difficult to be identified. The identification effect is most obvious for fissures larger than 16 cm under the same depth. In spring and summer, UAV infrared identification of mining fissures should be carried out during nighttime. This study provides the basis for the optimal time and season for the UAV infrared identification of different types of mining ground fissures.

基于图和流体扰动算法的多无人车编队及避障研究

为解决多无人车编队在存在运动目标、移动威胁与突发威胁等多种情况的复杂环境中的避障问题,设计了一种基于刚性图论和流体扰动算法的多无人车编队避障控制算法。首先,针对编队控制问题,采用图论方法描述多无人车之间的协同关系,利用无人车之间的距离约束,基于反步控制理论设计领航-跟随编队控制器。李雅普诺夫分析表明,期望的编队形状是渐近稳定的。其次,针对复杂动态障碍物环境下的编队避障问题,设计了基于流体扰动算法的避障路径规划方法,由领航无人车规划出编队的行驶路径,实现编队的整体避障。最后,基于MATLAB的仿真结果验证了所提算法的有效性。

一种多段机翼水面起降地效无人机气动特性

为改善水面起降性能和气动性能,基于船身式机身、多段机翼和T尾融合设计思路,提出并设计了一种新型仿生式多段机翼地效无人机方案,采用N-S方程和K-Ω-SST湍流模型,详细研究了该型无人机在不同状态下的压力云图、压力系数及升阻特性。仿真结果表明,地效作用随离水高度的增加而减小,离水高度与平均几何弦长之比(高度弦长比H/c)接近1时,地效作用较为显著,无人机在离水高度0.2 m时,升力系数、升阻比分别提升21.91%和40.37%,阻力系数降低15.22%;对提出的多段机翼布局,地效飞行主要影响下表面压力系数和压力云图,下表面压力系数展向上由内向外正压增幅逐渐减小,弦向上前后缘附近压力系数较小,结合压力云图分析,地效对升力增幅的影响主要集中在中段和内段机翼下方区域;地效飞行可明显提高升力线斜率(H/c为1时提高了8.89%),迎角增加时升力系数增幅和阻力系数降幅均逐渐变大,升阻比增幅(H/c为1)在迎角2°后均达到26%以上;通过验证机的多轮水面起降和有、无地效飞行试验,证明设计方案具有优秀的气动性能和飞行性能,可为水质检测、水面地效运输、搜救侦察等提供应用平台。

Unmanned air/ground vehicles heterogeneous cooperative techniques:Current status and prospects

Multiple unmanned air/ground vehicles heterogeneous cooperation is a novel and challenging filed.Heterogeneous cooperative techniques can widen the application fields of unmanned air or ground vehicles,and enhance the effectiveness of implementing detection,search and rescue tasks.This paper mainly focused on the key issues in multiple unmanned air/ground vehicles heterogeneous cooperation,including heterogeneous flocking,formation control,formation stability,network control,and actual applications.The main problems and future directions in this field were also analyzed in detail.These innovative technologies can significantly enhance the effectiveness of implementing complicated tasks,which definitely provide a series of novel breakthroughs for the intelligence,integration and advancement of future robot systems.

阿尔金地区“天空地深”一体化找矿勘查评价体系应用实践——以阿亚克-瓦石峡南为例

阿尔金地区属中高山地形,地势陡峭,海拔一般在2 000 m以上,比高大于300 m。高山区切割较深,地表基本无植被,基岩裸露较好。长期以来,因极端恶劣的交通条件,矿产勘查开发受到严重制约。新一轮找矿突破行动主攻昆仑-阿尔金,对高寒山区、工作空白区探索绿色、低成本、高效率的勘查评价技术方法势在必行,在此背景下逐渐形成适应阿尔金地区“天空地深”一体化找矿勘查评价体系[1]。该体系中“天”指遥感技术应用,即采用高清卫片结合谷歌地图等影像资料识别解译伟晶岩脉,圈定伟晶岩脉集中分布区域;“空”指无人机技术应用,即采用航空摄影及三维建模对地表伟晶岩延伸情况进行圈定;“地”指地面传统找矿手段应用,即采用路线地质调查、填图、槽探及采样分析等手段对无人机圈定的伟晶岩脉进行地面查证,圈定含矿伟晶岩脉;“深”指深部钻探手段应用,即采用便携式钻机了解含矿伟晶岩脉深部延伸及品位变化情况。该体系的应用可精准锁定找矿靶区,圈定矿(化)体,对矿区进行勘查评价,极大提高了找矿勘查效率,缩短找矿周期,在地形切割强烈的阿尔金地区值得广泛运用。

Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system

Ground-based platform systems provide a good tool for monitoring and managing crop conditions in precision agriculture applications and have been widely used for monitoring crop conditions.To develop an unmanned ground vehicle system(UGVS)based multi-sensors and test the feasibility of this system for monitoring rice conditions,an UGVS was developed to collect real-time rice condition information including NDVI values,reflectance measurements and crop canopy temperature in this study.Major components of the integrated system are GreenSeeker R100 system,hyper-spectroradiometer and infrared temperature sensor.The leaf area index(LAI)was measured by the CGMD302 Spectrometer.The Independent Samples T-Test method and the one way ANOVA method were used to determine the best spectral indices and analyze the relationship between the vegetation indices and rice LAI.It was found that the two best spectral indices for estimating LAI were NDVI(860 nm and 750 nm)with the correlation coefficient(R^(2))at 0.745 and RVI(853 nm and 751 nm)with the R^(2)at 0.724.The results show the UGVS can support multi-source information acquisition and is useful for crop management and precision agriculture applications.

无人机辅助智能边缘网络技术综述

简要介绍了无人机作为空基信息平台的主要应用场景,结合6G多接入网络和用户为中心网络架构,讨论了无人机在构建智能边缘网络中的重要作用。结合无人机平台在不同场景下的通、感、算能力特点,介绍无人机自组网、无人机通感一体化、无人机边缘计算等无人机辅助智能边缘网络关键技术。最后,围绕无人机在构建智能边缘网络过程中与无线接入网新技术结合时产生的新问题,探讨无人机通信信道建模、三维立体部署与路径规划、携能有限与续航问题、无人机网络安全、无人机集群异构网络融合等关键技术挑战和未来研究方向。

基于自由空间光的无人机通信网络关键技术与发展趋势

在当前电磁频谱拥堵和无线电干扰严重的情况下,基于自由空间光(FSO)的无人机(UAV)通信网络作为推进空天地一体化进程的重要一环,得到了学术界和工业界的广泛关注。与传统射频通信相比,FSO通信具有高数据传输速率、低时延和高安全性等优势。然而,FSO链路易受大气信道条件影响,同时UAV高移动性、网络高动态性以及机载资源的有限性给FSO的稳定连接与可靠通信带来了巨大挑战。因此,该文在介绍了FSO传输特性的基础上,着重分析了提升基于FSO的UAV通信网络稳定性与通信质量的关键技术,在此基础上,归纳出高可靠、强智能、长续航的发展趋势,以期为基于FSO的UAV通信网络发展提供参考与借鉴。

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approach–The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system.If dynamic obstacles move randomly in the operation field,the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations.This paper considers the construction of the FIS,which consists of two controllers.The first controller uses three sensors based on the obstacles distances from the front,right and left.The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay.The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings–Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS.The reported simulation results are obtained using MATLAB software package.The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently,even in a complex environment that is populated with dynamic obstacles.The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications–The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions.The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles.However,the methodology is approached using two-dimensional analyses.Hence,the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value–This paper presents the design of a FIS and its characterizations in dynamic environments,specifically for obstacles that move at different velocities.This facilitates an improved functionality of the operation of UGV.

基于优化DeepLabv3+的智能化高速铁路安全区域划分算法研究

针对目前电气化铁路沿线复杂背景下铁路安全区域划分均需采用实际固定标准件为参照物且区域划分范围小等问题,提出一种无需参照物的高速铁路安全区域划分算法.首先基于无人机所采集图像中的相关参数计算出相应的GSD(地面采样间距)参数,然后利用加入ECA-Net模块的DeepLabv3+模型对图像中的轨道进行精确分割.通过边缘检测、开运算、概率霍夫变换等一系列图像处理操作,提取出构成轨道的关键像素点,并运用最小二乘法进行轨道拟合,得出轨道数学表达式.最后,结合数学算法和GSD参数以及轨道数学表达式,完成安全区域的划分.实验结果表明,所提算法测量精度高达90%以上,无需选取固定参照物,适应性强、鲁棒性高,具有较高的实用性和可靠性.

UGV协同系统研究进展

无人地面车辆(UGV)在工业自动化、星球探索、灾后救援、智能交通以及军事作战等多任务领域都具有广阔的应用前景。UGV协同系统通过嵌入组织架构、合作策略、交互机制等协同内容,可以达到拓展环境感知范围、提高复杂环境理解适应能力和增强复杂任务工作效能的目的,受到了国内外广泛的关注。从UGV单体/群体体系结构、多重任务协同分配方法以及协同定位、编队、覆盖/探索等几个方面对目前国内外UGV协同工作关键技术进行了总结,给出了一个UGV协同系统的应用实例,并指出了系统发展趋势。

基于无人机倾斜摄影和激光扫描的离岸海岛高精度实景三维重建

本文面向离岸海岛实景三维需求与技术挑战,以珠海市庙湾岛为观测对象,采用无人机倾斜摄影与激光扫描的方式进行离岸海岛的实景三维数据获取。详细介绍了无人机任务规划、地面控制与实景三维数据处理工作,以及基于无参数布料模拟滤波的DEM生成,最终通过检查点验证了数据的绝对精度。结果表明,获取的正射、倾斜影像总中误差分别为14.72、6.60 cm;点云、DSM、DEM高程中误差分别为3.10、6.20、7.11 cm,满足离岸海岛实景三维展示与调查管理的需求。

基于动态蛇形-膨胀卷积模型的矿区地裂缝提取

采动裂缝是煤矿地下开采造成的破坏形式之一。针对矿区地表环境复杂、无人机图像裂缝提取方法精度较低的问题,本文融合动态蛇形卷积和膨胀卷积构建了全新的动态蛇形-膨胀卷积,并将其添加到现有分割模型的编码、解码结构,以优化整体网络模型结构;构建了矿区裂缝数据集,并在该自制数据集上进行裂缝提取精度验证。结果表明,加入动态蛇形-膨胀卷积,可使模型的分割精度(平均交并比)提高14.96%,对于实现地裂缝准确提取具有实践价值。

基于无人地面车辆补充采集的三维重构模型优化

为解决无人机单独采集图像生成三维(3D)重构模型存在破损和孔洞的问题,提出了一种基于无人地面车辆补充采集的3D模型优化方法。该方法耦合基于模型分辨率、三角网格结构和人工选点3种方法提取待优化区域,生成3D包围框和法向量信息,并利用3D重构质量启发式方法生成补充采集视点。结果表明:在该方法优化下,粗糙3D模型的低质量区域得到了显著改善,模型投影像素尺寸平均减少66%;该方法可以有效提升了3D模型重构质量,为室外大规模精细3D重构领域提供了可靠的解决方案。

基于强化学习的多无人车协同围捕方法

本文面向无人车协同围捕问题开展研究,提出了一种基于柔性执行者-评论家(SAC)算法框架的协同围捕算法.针对多无人车之间的协同性差的问题,在网络结构中加入长短期记忆(LSTM)构建记忆功能,帮助无人车利用历史观测序列进行更稳健的决策;针对网络结构中引入LSTM所导致的状态空间维度增大、效率低的问题,提出引入注意力机制,通过对状态空间进行注意力权重的计算和选择,将注意力集中在与任务相关的关键状态上,从而约束状态空间维度并保证网络的稳定性,实现多无人车之间稳定高效的合作并提高算法的训练效率.为解决协同围捕任务中奖励稀疏的问题,提出通过混合奖励函数将奖励函数分为个体奖励和协同奖励,通过引入个体奖励和协同奖励,无人车在围捕过程中可以获得更频繁的奖励信号.个体奖励通过引导无人车向目标靠近来激励其运动行为,而协同奖励则激励群体无人车共同完成围捕任务,从而进一步提高算法的收敛速度.最后,通过仿真和实验表明,该方法具有更快的收敛速度,相较于SAC算法,围捕时间缩短15.1%,成功率提升7.6%.

基于Frenet坐标下改进人工势场法的无人车局部路径规划

人工势场法由于运算量小、精度高,广泛应用于无人车的局部路径规划。针对传统人工势场法存在目标不可达、局部最小值及陷入U型障碍物的问题,提出一种基于Frenet坐标系下改进人工势场法的路径规划算法。构建Frenet坐标系来表述车辆避障运动,简化规划模型,解决路径规划中车辆与所在道路相对位置不易表述的问题。提出安全椭圆模型和预测距离的概念来调整势场影响区域,加入基于Frenet坐标系下的参考线势场及动态速度势场改进斥力场函数,解决车辆在静态和动态下的避障问题。利用数学仿真软件进行仿真,以不同车速在直道和弯道场景中对所提出的路径规划方法进行静态和动态避障仿真实验。研究结果表明:不同车速下的前轮转角、横摆角速度均控制在较小范围内,改进算法可以有效解决传统人工势场法的缺陷,同时与快速搜索随机树(Rapidly-exploring Random Tree,RRT)算法相比,其在避障过程中路径规划计算效率提高了42.8%,改进算法优势明显。

低空无人机与地面移动式激光雷达的点云配准研究

激光雷达多平台数据的匹配融合是目前点云应用的研究热点。本文以无人机与地面移动式激光雷达的配准为例,通过采集建筑用地、稀疏林地、茂密林地、规则林地的点云数据,研究手动配准、ICP配准对不同场景的适用性。实验表明,配准精度上,建筑用地>稀疏林地>茂密林地>规则林地,可见数据几何特征越强,配准效果越好。配准方法上,手动配准后叠加ICP的方法优于单一方法,可更好实现林区的点云匹配。